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Posted to commits@tvm.apache.org by tq...@apache.org on 2021/11/06 13:20:43 UTC
[tvm-site] branch asf-site updated: Docs build at Sat Nov 6
09:20:34 EDT 2021
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new c13b26b Docs build at Sat Nov 6 09:20:34 EDT 2021
c13b26b is described below
commit c13b26b87b09ac61878ee2f5131c59630925e5a8
Author: tqchen <tq...@gmail.com>
AuthorDate: Sat Nov 6 09:20:35 2021 -0400
Docs build at Sat Nov 6 09:20:34 EDT 2021
---
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649 files changed, 42443 insertions(+), 30934 deletions(-)
diff --git a/docs/_downloads/2fb9ae7bf124f72614a43137cf2919cb/micro_tflite.py b/docs/_downloads/2fb9ae7bf124f72614a43137cf2919cb/micro_tflite.py
index cab105c..35b08d8 100644
--- a/docs/_downloads/2fb9ae7bf124f72614a43137cf2919cb/micro_tflite.py
+++ b/docs/_downloads/2fb9ae7bf124f72614a43137cf2919cb/micro_tflite.py
@@ -269,10 +269,7 @@ os.unlink(model_library_format_tar_path)
import subprocess
import pathlib
-repo_root = pathlib.Path(
- subprocess.check_output(["git", "rev-parse", "--show-toplevel"], encoding="utf-8").strip()
-)
-template_project_path = repo_root / "src" / "runtime" / "crt" / "host"
+template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("crt"))
project_options = {} # You can use options to provide platform-specific options through TVM.
# Compiling for physical hardware (or an emulated board, like the mps_an521)
@@ -280,7 +277,7 @@ project_options = {} # You can use options to provide platform-specific options
# For physical hardware, you can try out the Zephyr platform by using a different template project
# and options:
#
-# template_project_path = repo_root / "apps" / "microtvm" / "zephyr" / "template_project"
+# template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr"))
# project_options = {"project_type": "host_driven", zephyr_board": "nucleo_f746zg"}}
# Create a temporary directory
diff --git a/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb b/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb
index 788fa2f..5ef732c 100644
--- a/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb
+++ b/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb
@@ -123,7 +123,7 @@
},
"outputs": [],
"source": [
- "with tvm.transform.PassContext(\n opt_level=3, config={\"tir.disable_vectorize\": True}, disabled_pass=[\"AlterOpLayout\"]\n):\n module = relay.build(mod, target=TARGET, params=params)\n\n\n# Inspecting the compilation output\n# ---------------------------------\n#\n# The compilation process has produced some C code implementing the operators in this graph. We\n# can inspect it by printing the CSourceModule contents (for the purposes of this tutorial, let's\n# just print t [...]
+ "with tvm.transform.PassContext(\n opt_level=3, config={\"tir.disable_vectorize\": True}, disabled_pass=[\"AlterOpLayout\"]\n):\n module = relay.build(mod, target=TARGET, params=params)\n\n\n# Inspecting the compilation output\n# ---------------------------------\n#\n# The compilation process has produced some C code implementing the operators in this graph. We\n# can inspect it by printing the CSourceModule contents (for the purposes of this tutorial, let's\n# just print t [...]
]
},
{
diff --git a/docs/_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py b/docs/_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py
index e7a1fa8..d310671 100644
--- a/docs/_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py
+++ b/docs/_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py
@@ -113,12 +113,9 @@ assert len(tasks) > 0
# choose other options by choosing from `PLATFORM` list.
#
-repo_root = pathlib.Path(
- subprocess.check_output(["git", "rev-parse", "--show-toplevel"], encoding="utf-8").strip()
-)
module_loader = tvm.micro.AutoTvmModuleLoader(
- template_project_dir=repo_root / "src" / "runtime" / "crt" / "host",
+ template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("crt")),
project_options={"verbose": False},
)
builder = tvm.autotvm.LocalBuilder(
@@ -134,7 +131,7 @@ measure_option = tvm.autotvm.measure_option(builder=builder, runner=runner)
# Compiling for physical hardware
# --------------------------------------------------------------------------
# module_loader = tvm.micro.AutoTvmModuleLoader(
-# template_project_dir=repo_root / "apps" / "microtvm" / "zephyr" / "template_project",
+# template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")),
# project_options={
# "zephyr_board": BOARD,
# "west_cmd": "west",
@@ -183,7 +180,7 @@ with pass_context:
temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
- str(repo_root / "src" / "runtime" / "crt" / "host"),
+ str(tvm.micro.get_microtvm_template_projects("crt")),
lowered,
temp_dir / "project",
{"verbose": False},
@@ -192,7 +189,7 @@ project = tvm.micro.generate_project(
# Compiling for physical hardware
# --------------------------------------------------------------------------
# project = tvm.micro.generate_project(
-# str(repo_root / "apps" / "microtvm" / "zephyr" / "template_project"),
+# str(tvm.micro.get_microtvm_template_projects("zephyr")),
# lowered,
# temp_dir / "project",
# {
@@ -226,7 +223,7 @@ with tvm.autotvm.apply_history_best("microtvm_autotune.log.txt"):
temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
- str(repo_root / "src" / "runtime" / "crt" / "host"),
+ str(tvm.micro.get_microtvm_template_projects("crt")),
lowered_tuned,
temp_dir / "project",
{"verbose": False},
@@ -235,7 +232,7 @@ project = tvm.micro.generate_project(
# Compiling for physical hardware
# --------------------------------------------------------------------------
# project = tvm.micro.generate_project(
-# str(repo_root / "apps" / "microtvm" / "zephyr" / "template_project"),
+# str(tvm.micro.get_microtvm_template_projects("zephyr")),
# lowered_tuned,
# temp_dir / "project",
# {
diff --git a/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb b/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb
index 86f7b55..8f83c5b 100644
--- a/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb
+++ b/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "repo_root = pathlib.Path(\n subprocess.check_output([\"git\", \"rev-parse\", \"--show-toplevel\"], encoding=\"utf-8\").strip()\n)\n\nmodule_loader = tvm.micro.AutoTvmModuleLoader(\n template_project_dir=repo_root / \"src\" / \"runtime\" / \"crt\" / \"host\",\n project_options={\"verbose\": False},\n)\nbuilder = tvm.autotvm.LocalBuilder(\n n_parallel=1,\n build_kwargs={\"build_option\": {\"tir.disable_vectorize\": True}},\n do_fork=True,\n build_func=tvm.micr [...]
+ "module_loader = tvm.micro.AutoTvmModuleLoader(\n template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects(\"crt\")),\n project_options={\"verbose\": False},\n)\nbuilder = tvm.autotvm.LocalBuilder(\n n_parallel=1,\n build_kwargs={\"build_option\": {\"tir.disable_vectorize\": True}},\n do_fork=True,\n build_func=tvm.micro.autotvm_build_func,\n)\nrunner = tvm.autotvm.LocalRunner(number=1, repeat=1, timeout=100, module_loader=module_loader)\n\nmea [...]
]
},
{
@@ -116,7 +116,7 @@
},
"outputs": [],
"source": [
- "with pass_context:\n lowered = tvm.relay.build(relay_mod, target=TARGET, params=params)\n\ntemp_dir = tvm.contrib.utils.tempdir()\n\nproject = tvm.micro.generate_project(\n str(repo_root / \"src\" / \"runtime\" / \"crt\" / \"host\"),\n lowered,\n temp_dir / \"project\",\n {\"verbose\": False},\n)\n\n# Compiling for physical hardware\n# --------------------------------------------------------------------------\n# project = tvm.micro.generate_project(\n# s [...]
+ "with pass_context:\n lowered = tvm.relay.build(relay_mod, target=TARGET, params=params)\n\ntemp_dir = tvm.contrib.utils.tempdir()\n\nproject = tvm.micro.generate_project(\n str(tvm.micro.get_microtvm_template_projects(\"crt\")),\n lowered,\n temp_dir / \"project\",\n {\"verbose\": False},\n)\n\n# Compiling for physical hardware\n# --------------------------------------------------------------------------\n# project = tvm.micro.generate_project(\n# str(tv [...]
]
},
{
@@ -134,7 +134,7 @@
},
"outputs": [],
"source": [
- "with tvm.autotvm.apply_history_best(\"microtvm_autotune.log.txt\"):\n with pass_context:\n lowered_tuned = tvm.relay.build(relay_mod, target=TARGET, params=params)\n\ntemp_dir = tvm.contrib.utils.tempdir()\n\nproject = tvm.micro.generate_project(\n str(repo_root / \"src\" / \"runtime\" / \"crt\" / \"host\"),\n lowered_tuned,\n temp_dir / \"project\",\n {\"verbose\": False},\n)\n\n# Compiling for physical hardware\n# ----------------------------------------- [...]
+ "with tvm.autotvm.apply_history_best(\"microtvm_autotune.log.txt\"):\n with pass_context:\n lowered_tuned = tvm.relay.build(relay_mod, target=TARGET, params=params)\n\ntemp_dir = tvm.contrib.utils.tempdir()\n\nproject = tvm.micro.generate_project(\n str(tvm.micro.get_microtvm_template_projects(\"crt\")),\n lowered_tuned,\n temp_dir / \"project\",\n {\"verbose\": False},\n)\n\n# Compiling for physical hardware\n# ---------------------------------------------- [...]
]
}
],
diff --git a/docs/_sources/dev/how_to/pytest_target_parametrization.rst.txt b/docs/_sources/dev/how_to/pytest_target_parametrization.rst.txt
index 6dfcaf3..3fbb694 100644
--- a/docs/_sources/dev/how_to/pytest_target_parametrization.rst.txt
+++ b/docs/_sources/dev/how_to/pytest_target_parametrization.rst.txt
@@ -21,7 +21,7 @@ Python Target Parametrization
Summary
-------
-For any supported runtime, TVM should should produce numerically
+For any supported runtime, TVM should produce numerically
correct results. Therefore, when writing unit tests that validate
the numeric output, these unit tests should be run on all supported
runtimes. Since this is a very common use case, TVM has helper
@@ -29,7 +29,7 @@ functions to parametrize unit tests such that they will run on all
targets that are enabled and have a compatible device.
A single python function in the test suite can expand to several
-parametrized unit tests, each of which tests a single target device.
+parameterized unit tests, each of which tests a single target device.
In order for a test to be run, all of the following must be true.
- The test exists in a file or directory that has been passed to
@@ -129,11 +129,11 @@ marks are as follows.
- ``@pytest.mark.gpu`` - Tags a function as using GPU
capabilities. This has no effect on its own, but can be paired with
command-line arguments ``-m gpu`` or ``-m 'not gpu'`` to restrict
- which tests pytest will executed. This should not be called on its
+ which tests pytest will execute. This should not be called on its
own, but is part of other marks used in unit-tests.
- ``@tvm.testing.uses_gpu`` - Applies ``@pytest.mark.gpu``. This
- should be used to mark a unit tests that may use the GPU, if one is
+ should be used to mark unit tests that may use the GPU, if one is
present. This decorator is only needed for tests that explicitly
loop over ``tvm.testing.enabled_targets()``, but that is no longer
the preferred style of writing unit tests (see below). When using
@@ -161,7 +161,7 @@ There also exists a ``tvm.testing.enabled_targets()`` that returns
all targets that are enabled and runnable on the current machine,
based on the environment variable ``TVM_TEST_TARGETS``, the build
configuration, and the physical hardware present. Most current tests
-explictly loop over the targets returned from ``enabled_targets()``,
+explicitly loop over the targets returned from ``enabled_targets()``,
but it should not be used for new tests. The pytest output for this
style silently skips runtimes that are disabled in ``config.cmake``,
or do not have a device on which they can run. In addition, the test
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index bb7136d..dfb46d7 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -287,7 +287,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 9.553 seconds)
+ **Total running time of the script:** ( 1 minutes 11.483 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 6236b14..fcfa7bd 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -380,7 +380,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 19.648 seconds)
+ **Total running time of the script:** ( 1 minutes 21.217 seconds)
.. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index 702352d..1a734b4 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,15 +5,15 @@
Computation times
=================
-**06:12.390** total execution time for **how_to_compile_models** files:
+**06:21.665** total execution time for **how_to_compile_models** files:
-- **01:19.648**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **01:09.553**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:54.464**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **00:51.984**: :ref:`sphx_glr_how_to_compile_models_from_caffe2.py` (``from_caffe2.py``)
-- **00:29.934**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:26.303**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:24.457**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:23.142**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:11.155**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:01.750**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:21.217**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **01:11.483**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:55.783**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **00:53.696**: :ref:`sphx_glr_how_to_compile_models_from_caffe2.py` (``from_caffe2.py``)
+- **00:30.687**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:26.879**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:25.078**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:23.661**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:11.349**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:01.831**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
diff --git a/docs/_sources/how_to/deploy/arm_compute_lib.rst.txt b/docs/_sources/how_to/deploy/arm_compute_lib.rst.txt
index 8314382..a7ec8b9 100644
--- a/docs/_sources/how_to/deploy/arm_compute_lib.rst.txt
+++ b/docs/_sources/how_to/deploy/arm_compute_lib.rst.txt
@@ -34,32 +34,31 @@ Before installing Arm Compute Library, it is important to know what architecture
to determine this is to use `lscpu` and look for the "Model name" of the CPU. You can then use this to
determine the architecture by looking online.
-We recommend two different ways to build and install ACL:
-
-* Use the script located at `docker/install/ubuntu_install_arm_compute_lib.sh`. You can use this
- script for building ACL from source natively or for cross-compiling the library on an x86 machine.
- You may need to change the architecture of the device you wish to compile for by altering the
- `target_arch` variable. Binaries will be built from source and installed to the location denoted by
- `install_path`.
-* Alternatively, you can download and use pre-built binaries from:
+TVM only supports a single version of ACL, currently this is v21.08, there are two recommended ways to build and install
+the required libraries:
+
+* Use the script located at `docker/install/ubuntu_download_arm_compute_lib_binaries.sh`. You can use this
+ script for downloading ACL binaries for the architecture and extensions specified in `target_lib`, these
+ will be installed to the location denoted by `install_path`.
+* Alternatively, you can download the pre-built binaries from:
https://github.com/ARM-software/ComputeLibrary/releases. When using this package, you will need to
- select the binaries for the architecture you require and make sure they are visible to cmake. This
- can be done like so:
+ select the binaries for the architecture and extensions you require, then make sure they are visible
+ to CMake:
.. code:: bash
cd <acl-prebuilt-package>/lib
- mv ./linux-<architecture-to-build-for>-neon/* .
+ mv ./<architecture-and-extensions-required>/* .
In both cases you will need to set USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR to the path where the ACL package
-is located. Cmake will look in /path-to-acl/ along with /path-to-acl/lib and /path-to-acl/build for the
+is located. CMake will look in /path-to-acl/ along with /path-to-acl/lib and /path-to-acl/build for the
required binaries. See the section below for more information on how to use these configuration options.
Building with ACL support
-------------------------
-The current implementation has two separate build options in cmake. The reason for this split is
+The current implementation has two separate build options in CMake. The reason for this split is
because ACL cannot be used on an x86 machine. However, we still want to be able compile an ACL
runtime module on an x86 machine.
@@ -73,7 +72,7 @@ need to use USE_ARM_COMPUTE_LIB=ON on the x86 machine and USE_ARM_COMPUTE_LIB_GR
AArch64 device.
By default both options are set to OFF. Using USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR=ON will mean that ACL
-binaries are searched for by cmake in the default locations
+binaries are searched for by CMake in the default locations
(see https://cmake.org/cmake/help/v3.4/command/find_library.html). In addition to this,
/path-to-tvm-project/acl/ will also be searched. It is likely that you will need to set your own path to
locate ACL. This can be done by specifying a path in the place of ON.
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index 6da1833..1b4ed65 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -393,7 +393,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 10.2961 10.1803 11.3268 10.0611 0.3590
+ 11.9578 11.6688 13.6466 11.5382 0.6720
diff --git a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
index 00d7cbd..ccb5ef2 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -247,7 +247,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 18.454 seconds)
+ **Total running time of the script:** ( 2 minutes 24.172 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
index 4e2745c..a7cf31d 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -334,7 +334,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 43.7388 43.2618 61.3751 42.8861 1.8866
+ 45.2401 44.8524 49.7695 44.4672 0.8217
@@ -374,7 +374,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.296 seconds)
+ **Total running time of the script:** ( 1 minutes 38.539 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
index b66a3ed..1220c88 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -352,7 +352,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 67.6108 67.1922 75.4519 66.3023 1.4117
+ 69.3439 69.0170 86.3369 68.2516 1.8214
@@ -386,7 +386,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 18.311 seconds)
+ **Total running time of the script:** ( 2 minutes 22.142 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
index 43b5bb0..9d0e16e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -200,7 +200,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 9.856 seconds)
+ **Total running time of the script:** ( 3 minutes 12.207 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index aba299a..f787cb8 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**11:28.760** total execution time for **how_to_deploy_models** files:
+**11:47.973** total execution time for **how_to_deploy_models** files:
-- **03:09.856**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **02:18.454**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:18.311**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:36.296**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:55.890**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:40.856**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:28.940**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.156**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **03:12.207**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **02:24.172**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:22.142**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:38.539**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:58.984**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:42.210**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:29.565**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.154**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index 1e876bc..03172ff 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
Computation times
=================
-**00:34.477** total execution time for **how_to_extend_tvm** files:
+**00:35.149** total execution time for **how_to_extend_tvm** files:
-- **00:31.129**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.109**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.078**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.161**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:31.761**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.150**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.081**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.158**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 70e220e..f3e5344 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
@@ -199,10 +199,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 5337us [5337us] (45.74%; 45.74%)
- FoldScaleAxis: 6330us [2us] (54.26%; 54.26%)
- FoldConstant: 6328us [1368us] (54.24%; 99.97%)
- InferType: 4960us [4960us] (42.51%; 78.38%)
+ InferType: 5431us [5431us] (46.27%; 46.27%)
+ FoldScaleAxis: 6306us [2us] (53.73%; 53.73%)
+ FoldConstant: 6304us [1297us] (53.71%; 99.97%)
+ InferType: 5007us [5007us] (42.66%; 79.42%)
@@ -239,10 +239,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 4949us [4949us] (43.04%; 43.04%)
- FoldScaleAxis: 6549us [2us] (56.96%; 56.96%)
- FoldConstant: 6548us [1335us] (56.95%; 99.97%)
- InferType: 5212us [5212us] (45.33%; 79.61%)
+ InferType: 5082us [5082us] (44.55%; 44.55%)
+ FoldScaleAxis: 6326us [2us] (55.45%; 55.45%)
+ FoldConstant: 6325us [1337us] (55.44%; 99.97%)
+ InferType: 4988us [4988us] (43.72%; 78.86%)
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index 1d08a27..9e1aea7 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -296,7 +296,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 41.937872 ms
+ Convolution: 54.269562 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
index ec4f9b9..978d8d6 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -518,8 +518,8 @@ by mapping the 2D convolution to tensor intrinsics
primfn(A_1: handle, W_1: handle, Conv_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], []),
- Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ buffers = {Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], []),
W: Buffer(W_2: Pointer(float16), float16, [3, 3, 16, 32, 16, 16], [])}
buffer_map = {A_1: A, W_1: W, Conv_1: Conv} {
attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 196;
@@ -614,7 +614,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 7.033960 ms
+ conv2d with tensor core: 7.699643 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
index 2964a0f..037ec96 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.007397
- Baseline: 3.271150
+ Numpy running time: 0.009229
+ Baseline: 3.340634
@@ -207,7 +207,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.285778
+ Opt1: 0.295032
@@ -302,7 +302,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.321452
+ Opt2: 0.331193
@@ -390,7 +390,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.112634
+ Opt3: 0.113024
@@ -505,7 +505,7 @@ flattening.
.. code-block:: none
- Opt4: 0.218060
+ Opt4: 0.232269
@@ -619,7 +619,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.200839
+ Opt5: 0.225938
@@ -643,8 +643,8 @@ Here is the generated IR after blocking.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
allocate(packedB: Pointer(global float32x32), float32x32, [32768]), storage_scope = global;
@@ -733,7 +733,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.059074
+ Opt6: 0.068730
@@ -757,8 +757,8 @@ Here is the generated IR after parallelization.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
- A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
allocate(packedB: Pointer(global float32x32), float32x32, [32768]), storage_scope = global {
diff --git a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
index 4031949..24c3bee 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:30.629** total execution time for **how_to_optimize_operators** files:
+**00:32.374** total execution time for **how_to_optimize_operators** files:
-- **00:28.400**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.166**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.062**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:29.986**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.281**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.107**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
index e855fff..9f8196d 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
Computation times
=================
-**04:10.173** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:10.891**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **00:54.964**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:31.129**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:18.318**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:07.574**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:07.298**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**04:13.504** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:13.406**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **00:55.457**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:31.830**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:17.732**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:07.667**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:07.412**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index b3d152f..797f6b8 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -746,7 +746,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.359 ms
+ Execution time of this operator: 0.362 ms
@@ -1346,7 +1346,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 10.891 seconds)
+ **Total running time of the script:** ( 2 minutes 13.406 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index 03eb579..4c4a3ee 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -614,7 +614,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.1213 3.1261 3.1348 3.1030 0.0134
+ 3.1554 3.1548 3.1623 3.1491 0.0054
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
index 2ad4508..0d96b60 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -633,7 +633,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 542.5096 542.6023 542.7230 542.2034 0.2220
+ 571.8012 571.6694 572.2598 571.4744 0.3339
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
index ad596ee..c1d22c2 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
@@ -356,34 +356,58 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {compute: Buffer(compute_2: Pointer(float32), float32, [128, 512], []),
- placeholder_4: Buffer(placeholder_10: Pointer(float32), float32, [128, 256], []),
+ placeholder_4: Buffer(placeholder_10: Pointer(float32), float32, [128, 512], []),
placeholder_3: Buffer(placeholder_11: Pointer(int32), int32, [33], []),
- placeholder_2: Buffer(placeholder_12: Pointer(float32), float32, [128, 512], []),
+ placeholder_2: Buffer(placeholder_12: Pointer(int32), int32, [4916], []),
placeholder: Buffer(placeholder_13: Pointer(float32), float32, [4916, 16, 1], []),
- placeholder_1: Buffer(placeholder_14: Pointer(int32), int32, [4916], [])}
- buffer_map = {placeholder_6: placeholder, placeholder_7: placeholder_1, placeholder_9: placeholder_2, placeholder_8: placeholder_3, compute_1: compute, placeholder_5: placeholder_4} {
+ placeholder_1: Buffer(placeholder_14: Pointer(float32), float32, [128, 256], [])}
+ buffer_map = {placeholder_6: placeholder, placeholder_5: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute} {
for (i0.outer.i1.outer.fused: int32, 0, 16) "parallel" {
allocate(compute_3: Pointer(global float32), float32, [4096]), storage_scope = global {
- for (i.outer.inner: int32, 0, 2) {
+ for (i.outer.inner: int32, 0, 16) {
for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 64) {
- for (j.init: int32, 0, 16) {
- compute_3[((((i.outer.inner*2048) + (i.inner.init*32)) + (nb_j.inner*16)) + j.init)] = 0f32
- }
+ for (i.inner.init: int32, 0, 8) {
+ compute_3[(((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16))] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 1)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 2)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 3)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 4)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 5)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 6)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 7)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 8)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 9)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 10)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 11)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 12)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 13)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 14)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 15)] = 0f32
}
for (elem_idx: int32, 0, ((int32*)placeholder_11[(((i0.outer.i1.outer.fused*2) + nb_j.inner) + 1)] - (int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)])) {
- for (i.inner: int32, 0, 64) {
- for (j: int32, 0, 16) {
- compute_3[((((i.outer.inner*2048) + (i.inner*32)) + (nb_j.inner*16)) + j)] = ((float32*)compute_3[((((i.outer.inner*2048) + (i.inner*32)) + (nb_j.inner*16)) + j)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_10[(((i.outer.inner*16384) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
- }
+ for (i.inner: int32, 0, 8) {
+ compute_3[(((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16))] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16))] + ((float32*)placeholder_13[(((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16))]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 1)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 1)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 1)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 2)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 2)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 2)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 3)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 3)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 3)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 4)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 4)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 4)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 5)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 5)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 5)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 6)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 6)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 6)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 7)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 7)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 7)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 8)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 8)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 8)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 9)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 9)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 9)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 10)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 10)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 10)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 11)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 11)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 11)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 12)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 12)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 12)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 13)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 13)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 13)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 14)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 14)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 14)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 15)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 15)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 15)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
}
}
}
}
for (i0.inner: int32, 0, 128) {
- for (i1.inner: int32, 0, 32) {
- compute_2[(((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)] = max(((float32*)compute_3[((i0.inner*32) + i1.inner)] + (float32*)placeholder_12[(((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)]), 0f32)
- }
+ compute_2[ramp(((i0.inner*512) + (i0.outer.i1.outer.fused*32)), 1, 32)] = max(((float32x32*)compute_3[ramp((i0.inner*32), 1, 32)] + (float32x32*)placeholder_10[ramp(((i0.inner*512) + (i0.outer.i1.outer.fused*32)), 1, 32)]), broadcast(0f32, 32))
}
}
}
@@ -437,7 +461,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 1.577 ms
+ Execution time of this operator: 1.847 ms
diff --git a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
index 98b237f..80e03ba 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:38.221** total execution time for **how_to_tune_with_autotvm** files:
+**00:40.937** total execution time for **how_to_tune_with_autotvm** files:
-- **00:37.517**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.184**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.177**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.172**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.171**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:40.250**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.186**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.169**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.167**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.166**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 285289d..425b590 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
@@ -242,26 +242,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 6 GFLOPS: 87.21/87.21 result: MeasureResult(costs=(0.0026544224565217393,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.523228645324707, timestamp=1635354481.0967414) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
- No: 7 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 8 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 9 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 10 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(TimeoutError(),), error_no=MeasureErrorNo.BUILD_TIMEOUT, all_cost=10, timestamp=1635354492.052086) [('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
- No: 11 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 12 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 13 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 14 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 15 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 16 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 17 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 18 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 19 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 60: 0xffffffffffffffff\n 59: _start\n 58: __libc_start_main\n 57: main\n 56: Py_Main\n 55: 0x000000000063886a\n 54: PyObject_Call\n 53: 0x000000000058945c\n 52: 0x0000000000507cd3\n 51: _PyEval_EvalFrameDefault\n 50: 0x000000000050a3fc\n 49: 0x00000000005099ff\n 48: 0x0000000000507cd3\n 47: _PyEval_EvalFrameDefault\n 46: 0x000000000050a22e\n 45: 0x0000000000 [...]
- No: 20 GFLOPS: 143.62/143.62 result: MeasureResult(costs=(0.00161187607,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2787423133850098, timestamp=1635354501.6196096) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+ No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 6 GFLOPS: 60.26/60.26 result: MeasureResult(costs=(0.003841781068965517,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.627082109451294, timestamp=1636191415.8784292) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+ No: 7 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 8 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 9 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 10 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(TimeoutError(),), error_no=MeasureErrorNo.BUILD_TIMEOUT, all_cost=10, timestamp=1636191426.8456671) [('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
+ No: 11 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 12 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 13 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 14 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 15 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 16 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 17 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 18 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 19 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 60: 0xffffffffffffffff\n 59: _start\n 58: __libc_start_main\n 57: main\n 56: Py_Main\n 55: 0x000000000063886a\n 54: PyObject_Call\n 53: 0x000000000058945c\n 52: 0x0000000000507cd3\n 51: _PyEval_EvalFrameDefault\n 50: 0x000000000050a3fc\n 49: 0x00000000005099ff\n 48: 0x0000000000507cd3\n 47: _PyEval_EvalFrameDefault\n 46: 0x000000000050a22e\n 45: 0x0000000000 [...]
+ No: 20 GFLOPS: 144.48/144.48 result: MeasureResult(costs=(0.001602333111111111,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.0883064270019531, timestamp=1636191436.3377938) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
@@ -314,7 +314,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
- Time cost of this operator: 0.001986
+ Time cost of this operator: 0.001954
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
index 64fb1b4..c234b2f 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -143,12 +143,9 @@ Configuring microTVM
.. code-block:: default
- repo_root = pathlib.Path(
- subprocess.check_output(["git", "rev-parse", "--show-toplevel"], encoding="utf-8").strip()
- )
module_loader = tvm.micro.AutoTvmModuleLoader(
- template_project_dir=repo_root / "src" / "runtime" / "crt" / "host",
+ template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("crt")),
project_options={"verbose": False},
)
builder = tvm.autotvm.LocalBuilder(
@@ -164,7 +161,7 @@ Configuring microTVM
# Compiling for physical hardware
# --------------------------------------------------------------------------
# module_loader = tvm.micro.AutoTvmModuleLoader(
- # template_project_dir=repo_root / "apps" / "microtvm" / "zephyr" / "template_project",
+ # template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")),
# project_options={
# "zephyr_board": BOARD,
# "west_cmd": "west",
@@ -222,7 +219,7 @@ Timing the untuned program
temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
- str(repo_root / "src" / "runtime" / "crt" / "host"),
+ str(tvm.micro.get_microtvm_template_projects("crt")),
lowered,
temp_dir / "project",
{"verbose": False},
@@ -231,7 +228,7 @@ Timing the untuned program
# Compiling for physical hardware
# --------------------------------------------------------------------------
# project = tvm.micro.generate_project(
- # str(repo_root / "apps" / "microtvm" / "zephyr" / "template_project"),
+ # str(tvm.micro.get_microtvm_template_projects("zephyr")),
# lowered,
# temp_dir / "project",
# {
@@ -266,10 +263,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 299.0 98.716 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 2.971 0.981 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.919 0.303 (1, 1, 10, 10, 3) 1 1
- Total_time - 302.89 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 309.9 98.726 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 3.076 0.98 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.922 0.294 (1, 1, 10, 10, 3) 1 1
+ Total_time - 313.898 - - - -
@@ -288,7 +285,7 @@ Timing the tuned program
temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
- str(repo_root / "src" / "runtime" / "crt" / "host"),
+ str(tvm.micro.get_microtvm_template_projects("crt")),
lowered_tuned,
temp_dir / "project",
{"verbose": False},
@@ -297,7 +294,7 @@ Timing the tuned program
# Compiling for physical hardware
# --------------------------------------------------------------------------
# project = tvm.micro.generate_project(
- # str(repo_root / "apps" / "microtvm" / "zephyr" / "template_project"),
+ # str(tvm.micro.get_microtvm_template_projects("zephyr")),
# lowered_tuned,
# temp_dir / "project",
# {
@@ -331,10 +328,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 173.6 98.47 (1, 1, 10, 10, 6) 2 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.891 1.072 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.806 0.457 (1, 3, 10, 10, 1) 1 1
- Total_time - 176.297 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 177.6 98.456 (1, 1, 10, 10, 6) 2 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.952 1.082 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.833 0.462 (1, 3, 10, 10, 1) 1 1
+ Total_time - 180.385 - - - -
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_tflite.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_tflite.rst.txt
index 8c73602..c79b0b6 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_tflite.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_tflite.rst.txt
@@ -316,10 +316,7 @@ Now, compile the model for the target:
import subprocess
import pathlib
- repo_root = pathlib.Path(
- subprocess.check_output(["git", "rev-parse", "--show-toplevel"], encoding="utf-8").strip()
- )
- template_project_path = repo_root / "src" / "runtime" / "crt" / "host"
+ template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("crt"))
project_options = {} # You can use options to provide platform-specific options through TVM.
# Compiling for physical hardware (or an emulated board, like the mps_an521)
@@ -327,7 +324,7 @@ Now, compile the model for the target:
# For physical hardware, you can try out the Zephyr platform by using a different template project
# and options:
#
- # template_project_path = repo_root / "apps" / "microtvm" / "zephyr" / "template_project"
+ # template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr"))
# project_options = {"project_type": "host_driven", zephyr_board": "nucleo_f746zg"}}
# Create a temporary directory
@@ -362,7 +359,7 @@ Now, compile the model for the target:
#ifdef __cplusplus
extern "C"
#endif
- TVM_DLL int32_t tvmgen_default_fused_nn_dense_add_nn_relu(void* args, void* arg_type_ids, int32_t num_args, void* out_ret_value, void* out_ret_tcode, void* resource_handle) {
+ TVM_DLL int32_t tvmgen_default_fused_nn_dense_add_nn_relu_1(void* args, void* arg_type_ids, int32_t num_args, void* out_ret_value, void* out_ret_tcode, void* resource_handle) {
void* arg0 = (((TVMValue*)args)[0].v_handle);
- .
- ./codegen
diff --git a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
index e272fb9..1bf361b 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:44.423** total execution time for **how_to_work_with_microtvm** files:
+**00:45.172** total execution time for **how_to_work_with_microtvm** files:
-- **00:40.952**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.314**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.157**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:41.823**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.195**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.154**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index 2b0b39c..84533cf 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:03.987** total execution time for **how_to_work_with_relay** files:
+**00:04.310** total execution time for **how_to_work_with_relay** files:
-- **00:02.454**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.532**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:02.640**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.670**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/scan.rst.txt b/docs/_sources/how_to/work_with_schedules/scan.rst.txt
index 83a4bb5..6ab2fc5 100644
--- a/docs/_sources/how_to/work_with_schedules/scan.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/scan.rst.txt
@@ -266,22 +266,22 @@ The following example demonstrates how we can build recurrence with two states.
primfn(X_1: handle, scan_2: handle, scan_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {X: Buffer(X_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- scan_1: Buffer(scan_4: Pointer(float32), float32, [m, l: int32], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {scan_1: Buffer(scan_4: Pointer(float32), float32, [m: int32, l: int32], [stride: int32, stride_1: int32], type="auto"),
+ X: Buffer(X_2: Pointer(float32), float32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
scan: Buffer(scan_5: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {X_1: X, scan_2: scan, scan_3: scan_1} {
for (i: int32, 0, n) {
- scan_5[(i*stride_5)] = (float32*)X_2[(i*stride_1)]
+ scan_5[(i*stride_5)] = (float32*)X_2[(i*stride_3)]
}
for (i_1: int32, 0, l) {
- scan_4[(i_1*stride_3)] = 0f32
+ scan_4[(i_1*stride_1)] = 0f32
}
for (scan.idx: int32, 0, (m - 1)) {
for (i_2: int32, 0, n) {
- scan_5[(((scan.idx + 1)*stride_4) + (i_2*stride_5))] = ((float32*)scan_5[((scan.idx*stride_4) + (i_2*stride_5))] + (float32*)X_2[(((scan.idx + 1)*stride) + (i_2*stride_1))])
+ scan_5[(((scan.idx + 1)*stride_4) + (i_2*stride_5))] = ((float32*)scan_5[((scan.idx*stride_4) + (i_2*stride_5))] + (float32*)X_2[(((scan.idx + 1)*stride_2) + (i_2*stride_3))])
}
for (i_3: int32, 0, l) {
- scan_4[(((scan.idx + 1)*stride_2) + (i_3*stride_3))] = ((float32*)scan_4[((scan.idx*stride_2) + (i_3*stride_3))] + (float32*)scan_5[(scan.idx*stride_4)])
+ scan_4[(((scan.idx + 1)*stride) + (i_3*stride_1))] = ((float32*)scan_4[((scan.idx*stride) + (i_3*stride_1))] + (float32*)scan_5[(scan.idx*stride_4)])
}
}
}
diff --git a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
index 4dfcf7f..2a89a5d 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**00:04.941** total execution time for **how_to_work_with_schedules** files:
+**00:05.020** total execution time for **how_to_work_with_schedules** files:
-- **00:01.757**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.128**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.598**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.595**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.296**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.195**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.188**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.185**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:01.792**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.068**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.669**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.657**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.288**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.186**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.185**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.175**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index 24c6a98..7db62e2 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -313,8 +313,8 @@ The importing needs to happen before the tensorized GEMV being executed.
A: Buffer(A_2: Pointer(float32), float32, [1024, 64], []),
B: Buffer(B_2: Pointer(float32), float32, [512, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp0um40v6k/input0.cc'
- source_filename = "/tmp/tmp0um40v6k/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpdl6d2pzt/input0.cc'
+ source_filename = "/tmp/tmpdl6d2pzt/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
diff --git a/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt b/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt
index c874b0c..27234d0 100644
--- a/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tuple_inputs.rst.txt
@@ -64,15 +64,15 @@ together in the next schedule procedure.
primfn(A0_1: handle, A1_1: handle, B_2: handle, B_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_4: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- B_1: Buffer(B_5: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+ buffers = {B_1: Buffer(B_4: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B: Buffer(B_5: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, B_2: B, B_3: B_1} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B_4[((i*stride) + (j*stride_1))] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
- B_5[((i*stride_4) + (j*stride_5))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
+ B_5[((i*stride_2) + (j*stride_3))] = ((float32*)A0_2[((i*stride_4) + (j*stride_5))] + 2f32)
+ B_4[((i*stride) + (j*stride_1))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
}
}
}
@@ -136,15 +136,15 @@ with :py:func:`te.comm_reducer` as below:
primfn(idx_1: handle, val_1: handle, T_2: handle, T_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {T_1: Buffer(T_4: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
- idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_1: int32, stride_2: int32], type="auto"),
- T: Buffer(T_5: Pointer(int32), int32, [m], [stride_3: int32], type="auto"),
+ T: Buffer(T_5: Pointer(int32), int32, [m], [stride_1: int32], type="auto"),
+ idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
val: Buffer(val_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {idx_1: idx, val_1: val, T_2: T, T_3: T_1} {
for (i: int32, 0, m) {
- T_5[(i*stride_3)] = -1
+ T_5[(i*stride_1)] = -1
T_4[(i*stride)] = -2147483648
for (k: int32, 0, n) {
- T_5[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T_4[(i*stride)]), (int32*)T_5[(i*stride_3)], (int32*)idx_2[((i*stride_1) + (k*stride_2))], dtype=int32)
+ T_5[(i*stride_1)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T_4[(i*stride)]), (int32*)T_5[(i*stride_1)], (int32*)idx_2[((i*stride_2) + (k*stride_3))], dtype=int32)
T_4[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T_4[(i*stride)]), (int32*)T_4[(i*stride)], (int32*)val_2[((i*stride_4) + (k*stride_5))], dtype=int32)
}
}
@@ -193,19 +193,19 @@ in terms of operation.
primfn(A0_1: handle, A1_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A0: Buffer(A0_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- C: Buffer(C_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, C_1: C} {
allocate(B.v0: Pointer(global float32), float32, [n]), storage_scope = global;
allocate(B.v1: Pointer(global float32), float32, [n]), storage_scope = global;
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B.v0[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))] + 2f32)
- B.v1[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))]*3f32)
+ B.v0[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
+ B.v1[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))]*3f32)
}
for (j_1: int32, 0, n) {
- C_2[((i*stride_2) + (j_1*stride_3))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
+ C_2[((i*stride) + (j_1*stride_1))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
}
}
}
diff --git a/docs/_sources/install/from_source.rst.txt b/docs/_sources/install/from_source.rst.txt
index 23be319..4fad42b 100644
--- a/docs/_sources/install/from_source.rst.txt
+++ b/docs/_sources/install/from_source.rst.txt
@@ -123,6 +123,9 @@ The configuration of TVM can be modified by editing `config.cmake` and/or by pas
- Note that apt-package append ``llvm-config`` with version number.
For example, set ``set(USE_LLVM llvm-config-10)`` if you installed LLVM 10 package
+ - If you are a PyTorch user, it is recommended to set ``(USE_LLVM "/path/to/llvm-config --link-static")`` and ``set(HIDE_PRIVATE_SYMBOLS ON)``
+ to avoid potential symbol conflicts between different versions LLVM used by TVM and PyTorch.
+
- We can then build tvm and related libraries.
.. code:: bash
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index df4090c..53b55ca 100644
--- a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:17.877** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:18.065** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:17.709**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.168**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:17.906**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.160**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index a684af5..04c74e9 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -263,7 +263,7 @@ The compilation steps are:
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:211: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
relay_prog, target=target, params=params, target_host=env.target_host
- resnet18_v1 inference graph built in 18.41s!
+ resnet18_v1 inference graph built in 18.69s!
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 38340c8..c4b20e8 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -299,7 +299,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:333: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 13.01s!
+ yolov3-tiny inference graph built in 13.15s!
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 8e8d42d..7e8542f 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**01:20.124** total execution time for **topic_vta_tutorials_frontend** files:
+**01:21.716** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:42.705**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:37.419**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:43.603**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:38.113**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
index 51d9878..d5e5bca 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:03.410** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.426** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:02.931**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.480**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.948**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.478**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
index ebf3ba3..a93b04e 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.872** total execution time for **topic_vta_tutorials** files:
+**00:00.880** total execution time for **topic_vta_tutorials** files:
-- **00:00.448**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.424**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.452**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.428**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt b/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt
index a9873b2..e26c238 100644
--- a/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/vta_get_started.rst.txt
@@ -425,8 +425,8 @@ with an :code:`env.alu` pragma.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], []),
- C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], []),
B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 560cd0b..5d61362 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -215,8 +215,8 @@ operator fusion.
primfn(A_1: handle, B_1: handle, C_1: handle, out_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {out: Buffer(out_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C, out_1: out} {
allocate(auto_scheduler_layout_transform: Pointer(global float32), float32, [1048576]), storage_scope = global {
@@ -298,7 +298,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 44.309 ms
+ Execution time of this operator: 45.418 ms
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index c0adac9..f607aa8 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -270,7 +270,7 @@ standard deviation.
.. code-block:: none
- {'mean': 229.1864895541221, 'median': 228.7280524149537, 'std': 1.0664440211813757}
+ {'mean': 249.92160882335156, 'median': 249.40169705078006, 'std': 1.3641753266557834}
@@ -484,31 +484,31 @@ the tuning data to.
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 33.79/ 49.04 GFLOPS | Progress: (4/10) | 5.66 s
[Task 1/25] Current/Best: 41.38/ 49.04 GFLOPS | Progress: (8/10) | 9.03 s
[Task 1/25] Current/Best: 45.56/ 49.04 GFLOPS | Progress: (10/10) | 9.93 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 20.37/ 37.69 GFLOPS | Progress: (4/10) | 2.48 s
[Task 2/25] Current/Best: 26.74/ 42.40 GFLOPS | Progress: (8/10) | 4.67 s
[Task 2/25] Current/Best: 28.10/ 42.40 GFLOPS | Progress: (10/10) | 5.96 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 15.73/ 46.67 GFLOPS | Progress: (4/10) | 2.94 s
[Task 3/25] Current/Best: 13.60/ 46.95 GFLOPS | Progress: (8/10) | 4.71 s
[Task 3/25] Current/Best: 14.43/ 46.95 GFLOPS | Progress: (10/10) | 5.63 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 46.48/ 46.48 GFLOPS | Progress: (4/10) | 6.34 s
[Task 4/25] Current/Best: 47.44/ 47.44 GFLOPS | Progress: (8/10) | 9.82 s
[Task 4/25] Current/Best: 27.66/ 47.44 GFLOPS | Progress: (10/10) | 12.63 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 36.66/ 41.53 GFLOPS | Progress: (4/10) | 2.66 s
[Task 5/25] Current/Best: 30.33/ 41.53 GFLOPS | Progress: (8/10) | 4.14 s
[Task 5/25] Current/Best: 38.20/ 41.53 GFLOPS | Progress: (10/10) | 4.86 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 43.09/ 43.09 GFLOPS | Progress: (4/10) | 4.14 s
[Task 6/25] Current/Best: 12.39/ 43.09 GFLOPS | Progress: (8/10) | 7.72 s
[Task 6/25] Current/Best: 29.24/ 43.90 GFLOPS | Progress: (10/10) | 8.57 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 22.33/ 32.23 GFLOPS | Progress: (4/10) | 3.38 s
[Task 7/25] Current/Best: 37.92/ 38.76 GFLOPS | Progress: (8/10) | 5.64 s
[Task 7/25] Current/Best: 33.23/ 38.76 GFLOPS | Progress: (10/10) | 6.57 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 18.19/ 32.72 GFLOPS | Progress: (4/10) | 3.28 s
[Task 8/25] Current/Best: 35.54/ 36.48 GFLOPS | Progress: (8/10) | 4.82 s
[Task 8/25] Current/Best: 4.93/ 36.48 GFLOPS | Progress: (10/10) | 5.94 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 12.76/ 28.64 GFLOPS | Progress: (4/10) | 31.94 s
[Task 9/25] Current/Best: 32.23/ 37.06 GFLOPS | Progress: (8/10) | 33.68 s
[Task 9/25] Current/Best: 27.35/ 37.06 GFLOPS | Progress: (10/10) | 34.87 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 12.10/ 35.24 GFLOPS | Progress: (4/10) | 3.50 s
[Task 10/25] Current/Best: 13.59/ 35.24 GFLOPS | Progress: (8/10) | 5.57 s
[Task 10/25] Current/Best: 27.16/ 37.43 GFLOPS | Progress: (10/10) | 6.27 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 36.83/ 40.62 GFLOPS | Progress: (4/10) | 2.95 s Done.
-
[Task 11/25] Current/Best: 37.60/ 48.82 GFLOPS | Progress: (8/10) | 4.87 s
[Task 11/25] Current/Best: 9.21/ 48.82 GFLOPS | Progress: (10/10) | 6.12 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 29.49/ 29.49 GFLOPS | Progress: (4/10) | 4.97 s
[Task 12/25] Current/Best: 36.71/ 37.16 GFLOPS | Progress: (8/10) | 6.89 s
[Task 12/25] Current/Best: 19.92/ 37.16 GFLOPS | Progress: (10/10) | 12.15 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 40.66/ 40.66 GFLOPS | Progress: (4/10) | 3.22 s
[Task 13/25] Current/Best: 29.30/ 45.89 GFLOPS | Progress: (8/10) | 6.02 s
[Task 13/25] Current/Best: 43.25/ 45.89 GFLOPS | Progress: (10/10) | 6.90 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 21.98/ 35.60 GFLOPS | Progress: (4/10) | 30.11 s
[Task 14/25] Current/Best: 44.27/ 44.27 GFLOPS | Progress: (8/10) | 58.72 s
[Task 14/25] Current/Best: 46.79/ 46.79 GFLOPS | Progress: (10/10) | 59.30 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 25.75/ 31.30 GFLOPS | Progress: (4/10) | 6.38 s
[Task 15/25] Current/Best: 36.74/ 36.74 GFLOPS | Progress: (8/10) | 35.10 s
[Task 15/25] Current/Best: 25.52/ 36.74 GFLOPS | Progress: (10/10) | 65.02 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 36.12/ 36.42 GFLOPS | Progress: (4/10) | 2.97 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 27.96/ 47.74 GFLOPS | Progress: (4/10) | 5.20 s
[Task 1/25] Current/Best: 26.59/ 47.74 GFLOPS | Progress: (8/10) | 8.44 s
[Task 1/25] Current/Best: 17.16/ 47.74 GFLOPS | Progress: (10/10) | 9.28 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 29.33/ 32.17 GFLOPS | Progress: (4/10) | 3.06 s
[Task 2/25] Current/Best: 31.85/ 32.17 GFLOPS | Progress: (8/10) | 4.49 s
[Task 2/25] Current/Best: 10.57/ 32.17 GFLOPS | Progress: (10/10) | 5.58 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 18.36/ 38.70 GFLOPS | Progress: (4/10) | 3.51 s
[Task 3/25] Current/Best: 26.54/ 44.18 GFLOPS | Progress: (8/10) | 5.79 s
[Task 3/25] Current/Best: 14.14/ 44.18 GFLOPS | Progress: (10/10) | 6.75 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 11.31/ 27.21 GFLOPS | Progress: (4/10) | 7.73 s
[Task 4/25] Current/Best: 25.66/ 45.19 GFLOPS | Progress: (8/10) | 9.26 s
[Task 4/25] Current/Best: 25.89/ 45.19 GFLOPS | Progress: (10/10) | 10.03 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 19.03/ 31.94 GFLOPS | Progress: (4/10) | 3.27 s
[Task 5/25] Current/Best: 16.94/ 40.84 GFLOPS | Progress: (8/10) | 5.33 s
[Task 5/25] Current/Best: 40.34/ 40.84 GFLOPS | Progress: (10/10) | 8.19 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 39.55/ 39.55 GFLOPS | Progress: (4/10) | 2.86 s
[Task 6/25] Current/Best: 11.95/ 39.55 GFLOPS | Progress: (8/10) | 7.35 s
[Task 6/25] Current/Best: 15.50/ 39.55 GFLOPS | Progress: (10/10) | 8.12 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 32.12/ 39.22 GFLOPS | Progress: (4/10) | 2.84 s
[Task 7/25] Current/Best: 27.27/ 39.22 GFLOPS | Progress: (8/10) | 4.52 s
[Task 7/25] Current/Best: 36.51/ 39.22 GFLOPS | Progress: (10/10) | 5.26 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 34.94/ 34.94 GFLOPS | Progress: (4/10) | 2.89 s
[Task 8/25] Current/Best: 27.22/ 34.94 GFLOPS | Progress: (8/10) | 8.62 s
[Task 8/25] Current/Best: 25.95/ 34.94 GFLOPS | Progress: (10/10) | 13.54 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 13.86/ 40.39 GFLOPS | Progress: (4/10) | 2.99 s
[Task 9/25] Current/Best: 29.43/ 44.64 GFLOPS | Progress: (8/10) | 4.89 s
[Task 9/25] Current/Best: 24.71/ 44.64 GFLOPS | Progress: (10/10) | 9.93 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 30.89/ 30.89 GFLOPS | Progress: (4/10) | 3.55 s
[Task 10/25] Current/Best: 34.88/ 35.31 GFLOPS | Progress: (8/10) | 4.77 s
[Task 10/25] Current/Best: 13.51/ 35.31 GFLOPS | Progress: (10/10) | 5.48 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 45.68/ 45.68 GFLOPS | Progress: (4/10) | 3.83 s
[Task 11/25] Current/Best: 38.01/ 45.68 GFLOPS | Progress: (8/10) | 5.52 s
[Task 11/25] Current/Best: 42.91/ 45.68 GFLOPS | Progress: (10/10) | 6.50 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 36.63/ 39.58 GFLOPS | Progress: (4/10) | 2.90 s
[Task 12/25] Current/Best: 19.09/ 40.93 GFLOPS | Progress: (8/10) | 8.30 s
[Task 12/25] Current/Best: 16.65/ 40.93 GFLOPS | Progress: (10/10) | 12.88 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 33.60/ 33.83 GFLOPS | Progress: (4/10) | 4.37 s
[Task 13/25] Current/Best: 37.78/ 37.78 GFLOPS | Progress: (8/10) | 6.84 s
[Task 13/25] Current/Best: 34.23/ 39.28 GFLOPS | Progress: (10/10) | 7.96 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 20.18/ 30.45 GFLOPS | Progress: (4/10) | 13.40 s
[Task 14/25] Current/Best: 33.35/ 33.35 GFLOPS | Progress: (8/10) | 15.68 s
[Task 14/25] Current/Best: 22.92/ 33.35 GFLOPS | Progress: (10/10) | 18.02 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+
[Task 15/25] Current/Best: 25.40/ 25.40 GFLOPS | Progress: (4/10) | 31.76 s
[Task 15/25] Current/Best: 27.29/ 46.30 GFLOPS | Progress: (8/10) | 61.34 s
[Task 15/25] Current/Best: 21.78/ 46.30 GFLOPS | Progress: (10/10) | 61.92 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 25.67/ 32.52 GFLOPS | Progress: (4/10) | 3.33 s
[Task 16/25] Current/Best: 35.96/ 39.86 GFLOPS | Progress: (8/10) | 8.51 s
[Task 16/25] Current/Best: 18.22/ 39.86 GFLOPS | Progress: (10/10) | 9.20 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 26.28/ 46.08 GFLOPS | Progress: (4/10) | 5.39 s
[Task 17/25] Current/Best: 43.30/ 46.08 GFLOPS | Progress: (8/10) | 9.06 s
[Task 17/25] Current/Best: 28.79/ 46.08 GFLOPS | Progress: (10/10) | 10.36 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 13.36/ 22.38 GFLOPS | Progress: (4/10) | 12.55 s
[Task 18/25] Current/Best: 23.44/ 29.70 GFLOPS | Progress: (8/10) | 23.53 s
[Task 18/25] Current/Best: 14.78/ 29.70 GFLOPS | Progress: (10/10) | 32.30 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 27.90/ 31.95 GFLOPS | Progress: (4/10) | 9.07 s
[Task 19/25] Current/Best: 17.73/ 37.48 GFLOPS | Progress: (8/10) | 16.89 s
[Task 19/25] Current/Best: 10.19/ 38.22 GFLOPS | Progress: (10/10) | 19.03 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 11.49/ 22.93 GFLOPS | Progress: (4/10) | 32.03 s
[Task 20/25] Current/Best: 0.00/ 22.93 GFLOPS | Progress: (8/10) | 219.53 s
[Task 20/25] Current/Best: 38.14/ 38.14 GFLOPS | Progress: (10/10) | 394.85 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+
[Task 21/25] Current/Best: 10.31/ 29.05 GFLOPS | Progress: (4/10) | 177.84 s
[Task 21/25] Current/Best: 34.68/ 34.68 GFLOPS | Progress: (8/10) | 180.43 s
[Task 21/25] Current/Best: 15.44/ 34.68 GFLOPS | Progress: (10/10) | 188.06 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 21.00/ 21.00 GFLOPS | Progress: (4/10) | 5.05 s
[Task 22/25] Current/Best: 29.20/ 38.97 GFLOPS | Progress: (8/10) | 6.32 s
[Task 22/25] Current/Best: 23.48/ 38.97 GFLOPS | Progress: (10/10) | 7.92 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 41.08/ 41.08 GFLOPS | Progress: (4/10) | 10.63 s
[Task 23/25] Current/Best: 42.96/ 42.96 GFLOPS | Progress: (8/10) | 19.45 s
[Task 23/25] Current/Best: 16.44/ 42.96 GFLOPS | Progress: (10/10) | 26.36 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 1.84/ 7.02 GFLOPS | Progress: (4/10) | 89.42 s
[Task 24/25] Current/Best: 6.55/ 7.85 GFLOPS | Progress: (8/10) | 111.82 s
[Task 24/25] Current/Best: 7.87/ 10.83 GFLOPS | Progress: (10/10) | 119.60 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
Done.
-
[Task 16/25] Current/Best: 35.04/ 36.42 GFLOPS | Progress: (8/10) | 4.19 s
[Task 16/25] Current/Best: 26.84/ 36.42 GFLOPS | Progress: (10/10) | 5.15 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 35.83/ 36.08 GFLOPS | Progress: (4/10) | 4.96 s
[Task 17/25] Current/Best: 21.70/ 36.08 GFLOPS | Progress: (8/10) | 7.76 s
[Task 17/25] Current/Best: 40.24/ 40.24 GFLOPS | Progress: (10/10) | 8.92 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 18.77/ 40.79 GFLOPS | Progress: (4/10) | 9.51 s
[Task 18/25] Current/Best: 40.74/ 40.79 GFLOPS | Progress: (8/10) | 11.68 s
[Task 18/25] Current/Best: 17.90/ 40.79 GFLOPS | Progress: (10/10) | 13.33 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 12.25/ 22.47 GFLOPS | Progress: (4/10) | 5.81 s
[Task 19/25] Current/Best: 9.31/ 24.96 GFLOPS | Progress: (8/10) | 14.89 s
[Task 19/25] Current/Best: 36.87/ 36.87 GFLOPS | Progress: (10/10) | 16.84 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 19.62/ 19.62 GFLOPS | Progress: (4/10) | 11.52 s
[Task 20/25] Current/Best: 19.99/ 19.99 GFLOPS | Progress: (8/10) | 187.76 s
[Task 20/25] Current/Best: 17.08/ 32.58 GFLOPS | Progress: (10/10) | 188.56 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 34.90/ 34.90 GFLOPS | Progress: (4/10) | 32.08 s
[Task 21/25] Current/Best: 25.95/ 34.90 GFLOPS | Progress: (8/10) | 34.98 s
[Task 21/25] Current/Best: 36.41/ 36.41 GFLOPS | Progress: (10/10) | 35.62 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 36.05/ 36.05 GFLOPS | Progress: (4/10) | 12.12 s
[Task 22/25] Current/Best: 9.91/ 36.44 GFLOPS | Progress: (8/10) | 13.40 s
[Task 22/25] Current/Best: 24.79/ 36.44 GFLOPS | Progress: (10/
10) | 14.32 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 17.40/ 23.26 GFLOPS | Progress: (4/10) | 4.70 s
[Task 23/25] Current/Best: 43.82/ 43.82 GFLOPS | Progress: (8/10) | 6.41 s
[Task 23/25] Current/Best: 26.00/ 43.82 GFLOPS | Progress: (10/10) | 7.45 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 9.45/ 9.45 GFLOPS | Progress: (4/10) | 6.93 s
[Task 24/25] Current/Best: 9.61/ 9.61 GFLOPS | Progress: (8/10) | 52.66 s
[Task 24/25] Current/Best: 12.27/ 12.27 GFLOPS | Progress: (10/10) | 53.69 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
Done.
- Done.
-
[Task 25/25] Current/Best: 3.13/ 3.13 GFLOPS | Progress: (4/10) | 3.17 s
[Task 25/25] Current/Best: 2.48/ 3.13 GFLOPS | Progress: (8/10) | 15.43 s
[Task 25/25] Current/Best: 0.00/ 3.13 GFLOPS | Progress: (10/10) | 45.72 s
+
[Task 25/25] Current/Best: 2.16/ 2.16 GFLOPS | Progress: (4/10) | 13.41 s
[Task 25/25] Current/Best: 2.96/ 2.96 GFLOPS | Progress: (8/10) | 25.92 s
[Task 25/25] Current/Best: 2.06/ 2.96 GFLOPS | Progress: (10/10) | 26.51 s
The output from this tuning process will look something like this:
@@ -597,7 +597,7 @@ Verify that the optimized model runs and produces the same results:
.. code-block:: none
class='n02123045 tabby, tabby cat' with probability=0.610552
- class='n02123159 tiger cat' with probability=0.367180
+ class='n02123159 tiger cat' with probability=0.367179
class='n02124075 Egyptian cat' with probability=0.019365
class='n02129604 tiger, Panthera tigris' with probability=0.001273
class='n04040759 radiator' with probability=0.000261
@@ -650,8 +650,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 211.9480087934062, 'median': 211.2688914872706, 'std': 1.1843122740378864}
- unoptimized: {'mean': 229.1864895541221, 'median': 228.7280524149537, 'std': 1.0664440211813757}
+ optimized: {'mean': 208.14901499077678, 'median': 207.8273080289364, 'std': 1.5755151130274634}
+ unoptimized: {'mean': 249.92160882335156, 'median': 249.40169705078006, 'std': 1.3641753266557834}
@@ -671,7 +671,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 13 minutes 32.640 seconds)
+ **Total running time of the script:** ( 19 minutes 58.259 seconds)
.. _sphx_glr_download_tutorial_autotvm_relay_x86.py:
diff --git a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
index a8dba25..56b12c5 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.178e-07 secs/op
+ 1.33e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index aeace81..7751ce0 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -228,7 +228,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0xa34f3b0)), stage(b, placeholder(b, 0x217ec360)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
+ [stage(a, placeholder(a, 0x4d31620)), stage(b, placeholder(b, 0x11fa0220)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 43bc00b..4d26b63 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**15:54.673** total execution time for **tutorial** files:
+**22:31.223** total execution time for **tutorial** files:
-- **13:32.640**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **00:58.936**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:36.634**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:29.982**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:15.214**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:00.545**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.480**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **19:58.259**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **00:58.153**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:39.625**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **00:30.904**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:22.489**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:00.999**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.552**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
- **00:00.164**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
- **00:00.028**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.026**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **00:00.025**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
- **00:00.025**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index a84bb42..c8d1523 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -233,7 +233,7 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000007
+ Numpy running time: 0.000008
naive: 0.000006
@@ -380,14 +380,14 @@ factor to be the number of threads on your CPU.
vector: 0.000016
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: Pointer(float32), float32, [n], [stride_1: int32], type="auto"),
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
+ C: Buffer(C_2: Pointer(float32), float32, [n], [stride_1: int32], type="auto"),
B: Buffer(B_2: Pointer(float32), float32, [n], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i.outer: int32, 0, floordiv((n + 3), 4)) "parallel" {
for (i.inner.s: int32, 0, 4) {
if @tir.likely((((i.outer*4) + i.inner.s) < n), dtype=bool) {
- C_2[(((i.outer*4) + i.inner.s)*stride)] = ((float32*)A_2[(((i.outer*4) + i.inner.s)*stride_1)] + (float32*)B_2[(((i.outer*4) + i.inner.s)*stride_2)])
+ C_2[(((i.outer*4) + i.inner.s)*stride_1)] = ((float32*)A_2[(((i.outer*4) + i.inner.s)*stride)] + (float32*)B_2[(((i.outer*4) + i.inner.s)*stride_2)])
}
}
}
@@ -425,10 +425,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 6.94196205586195e-06 1.0
- naive 5.6450999999999995e-06 0.8131850843571162
- parallel 5.1746e-06 0.7454088567986987
- vector 1.58908e-05 2.289093468406594
+ numpy 7.98278022557497e-06 1.0
+ naive 5.9189e-06 0.7414584684465222
+ parallel 4.9771999999999995e-06 0.6234920490550659
+ vector 1.6127399999999997e-05 2.0202735819196875
@@ -817,7 +817,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.007339
+ Numpy running time: 0.009308
@@ -873,7 +873,7 @@ optimizations.
.. code-block:: none
- none: 3.270636
+ none: 3.109406
@@ -969,7 +969,7 @@ schedule.
.. code-block:: none
- blocking: 0.283921
+ blocking: 0.291928
@@ -995,8 +995,8 @@ internal representation and compare it to the original:
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
- A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x.outer: int32, 0, 32) {
@@ -1057,7 +1057,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.321941
+ vectorization: 0.331263
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1125,7 +1125,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.110927
+ loop permutation: 0.113750
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1218,7 +1218,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.215637
+ array packing: 0.224114
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1305,7 +1305,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.199154
+ block caching: 0.224213
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1382,7 +1382,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.058826
+ parallelization: 0.067949
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1454,13 +1454,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.2706357621000004 1.0
- blocking 0.2839211449 0.08680916052776867
- vectorization 0.3219410705 0.09843378899926447
- loop permutation 0.1109266605 0.033915932121030966
- array packing 0.2156372384 0.065931291065424
- block caching 0.19915391919999997 0.06089150051735746
- parallelization 0.05882565829999999 0.017986001064890633
+ none 3.1094061458 1.0
+ blocking 0.29192816779999997 0.09388550549895809
+ vectorization 0.3312631714 0.10653583220302389
+ loop permutation 0.1137497149 0.036582456445468314
+ array packing 0.2241142794 0.07207623221003798
+ block caching 0.22421289339999997 0.07210794694763607
+ parallelization 0.0679485881 0.021852593361526892
diff --git a/docs/api/links.html b/docs/api/links.html
new file mode 100644
index 0000000..dad4f93
--- /dev/null
+++ b/docs/api/links.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/api/links.html" />
+ <script>
+ window.location.href = "../reference/api/links.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/auto_scheduler.html b/docs/api/python/auto_scheduler.html
new file mode 100644
index 0000000..f632fbb
--- /dev/null
+++ b/docs/api/python/auto_scheduler.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/auto_scheduler.html" />
+ <script>
+ window.location.href = "../../reference/api/python/auto_scheduler.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/autotvm.html b/docs/api/python/autotvm.html
new file mode 100644
index 0000000..f5609e4
--- /dev/null
+++ b/docs/api/python/autotvm.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/autotvm.html" />
+ <script>
+ window.location.href = "../../reference/api/python/autotvm.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/contrib.html b/docs/api/python/contrib.html
new file mode 100644
index 0000000..d1f8a2e
--- /dev/null
+++ b/docs/api/python/contrib.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/contrib.html" />
+ <script>
+ window.location.href = "../../reference/api/python/contrib.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/driver.html b/docs/api/python/driver.html
new file mode 100644
index 0000000..26f458b
--- /dev/null
+++ b/docs/api/python/driver.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/driver.html" />
+ <script>
+ window.location.href = "../../reference/api/python/driver.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/error.html b/docs/api/python/error.html
new file mode 100644
index 0000000..143dda4
--- /dev/null
+++ b/docs/api/python/error.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/error.html" />
+ <script>
+ window.location.href = "../../reference/api/python/error.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/graph_executor.html b/docs/api/python/graph_executor.html
new file mode 100644
index 0000000..7451482
--- /dev/null
+++ b/docs/api/python/graph_executor.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/graph_executor.html" />
+ <script>
+ window.location.href = "../../reference/api/python/graph_executor.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/index.html b/docs/api/python/index.html
new file mode 100644
index 0000000..4a98376
--- /dev/null
+++ b/docs/api/python/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/index.html" />
+ <script>
+ window.location.href = "../../reference/api/python/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/ir.html b/docs/api/python/ir.html
new file mode 100644
index 0000000..dccedbd
--- /dev/null
+++ b/docs/api/python/ir.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/ir.html" />
+ <script>
+ window.location.href = "../../reference/api/python/ir.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/micro.html b/docs/api/python/micro.html
new file mode 100644
index 0000000..c0b5c20
--- /dev/null
+++ b/docs/api/python/micro.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/micro.html" />
+ <script>
+ window.location.href = "../../reference/api/python/micro.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/ndarray.html b/docs/api/python/ndarray.html
new file mode 100644
index 0000000..0c7c4d3
--- /dev/null
+++ b/docs/api/python/ndarray.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/ndarray.html" />
+ <script>
+ window.location.href = "../../reference/api/python/ndarray.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/analysis.html b/docs/api/python/relay/analysis.html
new file mode 100644
index 0000000..7e921dd
--- /dev/null
+++ b/docs/api/python/relay/analysis.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/analysis.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/analysis.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/backend.html b/docs/api/python/relay/backend.html
new file mode 100644
index 0000000..0448c11
--- /dev/null
+++ b/docs/api/python/relay/backend.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/backend.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/backend.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/dataflow_pattern.html b/docs/api/python/relay/dataflow_pattern.html
new file mode 100644
index 0000000..8d96d34
--- /dev/null
+++ b/docs/api/python/relay/dataflow_pattern.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/dataflow_pattern.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/dataflow_pattern.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/frontend.html b/docs/api/python/relay/frontend.html
new file mode 100644
index 0000000..a1425d2
--- /dev/null
+++ b/docs/api/python/relay/frontend.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/frontend.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/frontend.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/image.html b/docs/api/python/relay/image.html
new file mode 100644
index 0000000..aeb4f67
--- /dev/null
+++ b/docs/api/python/relay/image.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/image.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/image.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/index.html b/docs/api/python/relay/index.html
new file mode 100644
index 0000000..2f3fb5e
--- /dev/null
+++ b/docs/api/python/relay/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/index.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/nn.html b/docs/api/python/relay/nn.html
new file mode 100644
index 0000000..d2e10d5
--- /dev/null
+++ b/docs/api/python/relay/nn.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/nn.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/nn.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/relay/testing.html b/docs/api/python/relay/testing.html
new file mode 100644
index 0000000..3ce810a
--- /dev/null
+++ b/docs/api/python/relay/testing.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/testing.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/testing.html"
+ </script>
+ </head>
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diff --git a/docs/api/python/relay/transform.html b/docs/api/python/relay/transform.html
new file mode 100644
index 0000000..b50a68e
--- /dev/null
+++ b/docs/api/python/relay/transform.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/transform.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/transform.html"
+ </script>
+ </head>
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diff --git a/docs/api/python/relay/vision.html b/docs/api/python/relay/vision.html
new file mode 100644
index 0000000..1335481
--- /dev/null
+++ b/docs/api/python/relay/vision.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/relay/vision.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/relay/vision.html"
+ </script>
+ </head>
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diff --git a/docs/api/python/rpc.html b/docs/api/python/rpc.html
new file mode 100644
index 0000000..a67a107
--- /dev/null
+++ b/docs/api/python/rpc.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/rpc.html" />
+ <script>
+ window.location.href = "../../reference/api/python/rpc.html"
+ </script>
+ </head>
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diff --git a/docs/api/python/runtime.html b/docs/api/python/runtime.html
new file mode 100644
index 0000000..e1b2f00
--- /dev/null
+++ b/docs/api/python/runtime.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/runtime.html" />
+ <script>
+ window.location.href = "../../reference/api/python/runtime.html"
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+ </head>
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new file mode 100644
index 0000000..bb76c46
--- /dev/null
+++ b/docs/api/python/target.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/target.html" />
+ <script>
+ window.location.href = "../../reference/api/python/target.html"
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+ </head>
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new file mode 100644
index 0000000..6b88c8c
--- /dev/null
+++ b/docs/api/python/te.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/te.html" />
+ <script>
+ window.location.href = "../../reference/api/python/te.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/tir.html b/docs/api/python/tir.html
new file mode 100644
index 0000000..fbbaeb9
--- /dev/null
+++ b/docs/api/python/tir.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/tir.html" />
+ <script>
+ window.location.href = "../../reference/api/python/tir.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/api/python/topi.html b/docs/api/python/topi.html
new file mode 100644
index 0000000..c9301fd
--- /dev/null
+++ b/docs/api/python/topi.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../reference/api/python/topi.html" />
+ <script>
+ window.location.href = "../../reference/api/python/topi.html"
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+ </head>
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new file mode 100644
index 0000000..75c10bb
--- /dev/null
+++ b/docs/api/python/vta/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
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+ <head>
+ <meta http-equiv="refresh" content="1; url=../../../reference/api/python/vta/index.html" />
+ <script>
+ window.location.href = "../../../reference/api/python/vta/index.html"
+ </script>
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new file mode 100644
index 0000000..c568fad
--- /dev/null
+++ b/docs/deploy/android.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/android.html" />
+ <script>
+ window.location.href = "../how_to/deploy/android.html"
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+ </head>
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diff --git a/docs/deploy/arm_compute_lib.html b/docs/deploy/arm_compute_lib.html
new file mode 100644
index 0000000..f12a093
--- /dev/null
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@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
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+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/arm_compute_lib.html" />
+ <script>
+ window.location.href = "../how_to/deploy/arm_compute_lib.html"
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+ </head>
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diff --git a/docs/deploy/bnns.html b/docs/deploy/bnns.html
new file mode 100644
index 0000000..d6a6756
--- /dev/null
+++ b/docs/deploy/bnns.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
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+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/bnns.html" />
+ <script>
+ window.location.href = "../how_to/deploy/bnns.html"
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new file mode 100644
index 0000000..475fde2
--- /dev/null
+++ b/docs/deploy/cpp_deploy.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/cpp_deploy.html" />
+ <script>
+ window.location.href = "../how_to/deploy/cpp_deploy.html"
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+ </head>
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new file mode 100644
index 0000000..3956d49
--- /dev/null
+++ b/docs/deploy/hls.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/hls.html" />
+ <script>
+ window.location.href = "../how_to/deploy/hls.html"
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new file mode 100644
index 0000000..87d19c0
--- /dev/null
+++ b/docs/deploy/index.html
@@ -0,0 +1,10 @@
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+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/index.html" />
+ <script>
+ window.location.href = "../how_to/deploy/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/deploy/integrate.html b/docs/deploy/integrate.html
new file mode 100644
index 0000000..3e6c45a
--- /dev/null
+++ b/docs/deploy/integrate.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/integrate.html" />
+ <script>
+ window.location.href = "../how_to/deploy/integrate.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/deploy/tensorrt.html b/docs/deploy/tensorrt.html
new file mode 100644
index 0000000..0c11630
--- /dev/null
+++ b/docs/deploy/tensorrt.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/tensorrt.html" />
+ <script>
+ window.location.href = "../how_to/deploy/tensorrt.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/deploy/vitis_ai.html b/docs/deploy/vitis_ai.html
new file mode 100644
index 0000000..16595f9
--- /dev/null
+++ b/docs/deploy/vitis_ai.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/deploy/vitis_ai.html" />
+ <script>
+ window.location.href = "../how_to/deploy/vitis_ai.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/benchmark.html b/docs/dev/benchmark.html
new file mode 100644
index 0000000..31e687e
--- /dev/null
+++ b/docs/dev/benchmark.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/benchmark.html" />
+ <script>
+ window.location.href = "../arch/benchmark.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/codebase_walkthrough.html b/docs/dev/codebase_walkthrough.html
new file mode 100644
index 0000000..511e8f0
--- /dev/null
+++ b/docs/dev/codebase_walkthrough.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=tutorial/codebase_walkthrough.html" />
+ <script>
+ window.location.href = "tutorial/codebase_walkthrough.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/convert_layout.html b/docs/dev/convert_layout.html
new file mode 100644
index 0000000..d478a1b
--- /dev/null
+++ b/docs/dev/convert_layout.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/convert_layout.html" />
+ <script>
+ window.location.href = "../arch/convert_layout.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/debugger.html b/docs/dev/debugger.html
new file mode 100644
index 0000000..4cf2f9e
--- /dev/null
+++ b/docs/dev/debugger.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/debugger.html" />
+ <script>
+ window.location.href = "../arch/debugger.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/device_target_interactions.html b/docs/dev/device_target_interactions.html
new file mode 100644
index 0000000..a3ac67c
--- /dev/null
+++ b/docs/dev/device_target_interactions.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/device_target_interactions.html" />
+ <script>
+ window.location.href = "../arch/device_target_interactions.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/frontend/tensorflow.html b/docs/dev/frontend/tensorflow.html
new file mode 100644
index 0000000..8909baa
--- /dev/null
+++ b/docs/dev/frontend/tensorflow.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../arch/frontend/tensorflow.html" />
+ <script>
+ window.location.href = "../../arch/frontend/tensorflow.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/how_to.html b/docs/dev/how_to.html
new file mode 100644
index 0000000..f2e9c2e
--- /dev/null
+++ b/docs/dev/how_to.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=index.html" />
+ <script>
+ window.location.href = "index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/how_to/pytest_target_parametrization.html b/docs/dev/how_to/pytest_target_parametrization.html
index c037ce6..c9a9b44 100644
--- a/docs/dev/how_to/pytest_target_parametrization.html
+++ b/docs/dev/how_to/pytest_target_parametrization.html
@@ -319,14 +319,14 @@
<h1>Python Target Parametrization<a class="headerlink" href="#python-target-parametrization" title="Permalink to this headline">¶</a></h1>
<div class="section" id="summary">
<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
-<p>For any supported runtime, TVM should should produce numerically
+<p>For any supported runtime, TVM should produce numerically
correct results. Therefore, when writing unit tests that validate
the numeric output, these unit tests should be run on all supported
runtimes. Since this is a very common use case, TVM has helper
functions to parametrize unit tests such that they will run on all
targets that are enabled and have a compatible device.</p>
<p>A single python function in the test suite can expand to several
-parametrized unit tests, each of which tests a single target device.
+parameterized unit tests, each of which tests a single target device.
In order for a test to be run, all of the following must be true.</p>
<ul class="simple">
<li><p>The test exists in a file or directory that has been passed to
@@ -410,10 +410,10 @@ marks are as follows.</p>
<li><p><code class="docutils literal notranslate"><span class="pre">@pytest.mark.gpu</span></code> - Tags a function as using GPU
capabilities. This has no effect on its own, but can be paired with
command-line arguments <code class="docutils literal notranslate"><span class="pre">-m</span> <span class="pre">gpu</span></code> or <code class="docutils literal notranslate"><span class="pre">-m</span> <span class="pre">'not</span> <span class="pre">gpu'</span></code> to restrict
-which tests pytest will executed. This should not be called on its
+which tests pytest will execute. This should not be called on its
own, but is part of other marks used in unit-tests.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">@tvm.testing.uses_gpu</span></code> - Applies <code class="docutils literal notranslate"><span class="pre">@pytest.mark.gpu</span></code>. This
-should be used to mark a unit tests that may use the GPU, if one is
+should be used to mark unit tests that may use the GPU, if one is
present. This decorator is only needed for tests that explicitly
loop over <code class="docutils literal notranslate"><span class="pre">tvm.testing.enabled_targets()</span></code>, but that is no longer
the preferred style of writing unit tests (see below). When using
@@ -438,7 +438,7 @@ listed as skipped.</p>
all targets that are enabled and runnable on the current machine,
based on the environment variable <code class="docutils literal notranslate"><span class="pre">TVM_TEST_TARGETS</span></code>, the build
configuration, and the physical hardware present. Most current tests
-explictly loop over the targets returned from <code class="docutils literal notranslate"><span class="pre">enabled_targets()</span></code>,
+explicitly loop over the targets returned from <code class="docutils literal notranslate"><span class="pre">enabled_targets()</span></code>,
but it should not be used for new tests. The pytest output for this
style silently skips runtimes that are disabled in <code class="docutils literal notranslate"><span class="pre">config.cmake</span></code>,
or do not have a device on which they can run. In addition, the test
diff --git a/docs/dev/hybrid_script.html b/docs/dev/hybrid_script.html
new file mode 100644
index 0000000..b4043b5
--- /dev/null
+++ b/docs/dev/hybrid_script.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/hybrid_script.html" />
+ <script>
+ window.location.href = "../arch/hybrid_script.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/index.html b/docs/dev/index.html
new file mode 100644
index 0000000..f99695f
--- /dev/null
+++ b/docs/dev/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/index.html" />
+ <script>
+ window.location.href = "../arch/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/inferbound.html b/docs/dev/inferbound.html
new file mode 100644
index 0000000..2cec43e
--- /dev/null
+++ b/docs/dev/inferbound.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/inferbound.html" />
+ <script>
+ window.location.href = "../arch/inferbound.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/introduction_to_module_serialization.html b/docs/dev/introduction_to_module_serialization.html
new file mode 100644
index 0000000..872b0f8
--- /dev/null
+++ b/docs/dev/introduction_to_module_serialization.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/introduction_to_module_serialization.html" />
+ <script>
+ window.location.href = "../arch/introduction_to_module_serialization.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/microtvm_design.html b/docs/dev/microtvm_design.html
new file mode 100644
index 0000000..db5a1bb
--- /dev/null
+++ b/docs/dev/microtvm_design.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/microtvm_design.html" />
+ <script>
+ window.location.href = "../arch/microtvm_design.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/model_library_format.html b/docs/dev/model_library_format.html
new file mode 100644
index 0000000..71e27ef
--- /dev/null
+++ b/docs/dev/model_library_format.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/model_library_format.html" />
+ <script>
+ window.location.href = "../arch/model_library_format.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/pass_infra.html b/docs/dev/pass_infra.html
new file mode 100644
index 0000000..06ae8b5
--- /dev/null
+++ b/docs/dev/pass_infra.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/pass_infra.html" />
+ <script>
+ window.location.href = "../arch/pass_infra.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/pytest_target_parametrization.html b/docs/dev/pytest_target_parametrization.html
new file mode 100644
index 0000000..1d0d6c9
--- /dev/null
+++ b/docs/dev/pytest_target_parametrization.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=how_to/pytest_target_parametrization.html" />
+ <script>
+ window.location.href = "how_to/pytest_target_parametrization.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/relay_add_op.html b/docs/dev/relay_add_op.html
new file mode 100644
index 0000000..4ef68f2
--- /dev/null
+++ b/docs/dev/relay_add_op.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=how_to/relay_add_op.html" />
+ <script>
+ window.location.href = "how_to/relay_add_op.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/relay_add_pass.html b/docs/dev/relay_add_pass.html
new file mode 100644
index 0000000..d71f76e
--- /dev/null
+++ b/docs/dev/relay_add_pass.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=how_to/relay_add_pass.html" />
+ <script>
+ window.location.href = "how_to/relay_add_pass.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/relay_bring_your_own_codegen.html b/docs/dev/relay_bring_your_own_codegen.html
new file mode 100644
index 0000000..8aa3373
--- /dev/null
+++ b/docs/dev/relay_bring_your_own_codegen.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=how_to/relay_bring_your_own_codegen.html" />
+ <script>
+ window.location.href = "how_to/relay_bring_your_own_codegen.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/relay_intro.html b/docs/dev/relay_intro.html
new file mode 100644
index 0000000..d6b9233
--- /dev/null
+++ b/docs/dev/relay_intro.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/relay_intro.html" />
+ <script>
+ window.location.href = "../arch/relay_intro.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/relay_op_strategy.html b/docs/dev/relay_op_strategy.html
new file mode 100644
index 0000000..720f11b
--- /dev/null
+++ b/docs/dev/relay_op_strategy.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/relay_op_strategy.html" />
+ <script>
+ window.location.href = "../arch/relay_op_strategy.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/runtime.html b/docs/dev/runtime.html
new file mode 100644
index 0000000..6bbfa7d
--- /dev/null
+++ b/docs/dev/runtime.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/runtime.html" />
+ <script>
+ window.location.href = "../arch/runtime.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/runtimes/vulkan.html b/docs/dev/runtimes/vulkan.html
new file mode 100644
index 0000000..020a69c
--- /dev/null
+++ b/docs/dev/runtimes/vulkan.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../../arch/runtimes/vulkan.html" />
+ <script>
+ window.location.href = "../../arch/runtimes/vulkan.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/security.html b/docs/dev/security.html
new file mode 100644
index 0000000..6a8755f
--- /dev/null
+++ b/docs/dev/security.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/security.html" />
+ <script>
+ window.location.href = "../arch/security.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/dev/virtual_machine.html b/docs/dev/virtual_machine.html
new file mode 100644
index 0000000..6d30679
--- /dev/null
+++ b/docs/dev/virtual_machine.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../arch/virtual_machine.html" />
+ <script>
+ window.location.href = "../arch/virtual_machine.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/genindex.html b/docs/genindex.html
index 6857cf2..368ab5d 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -884,10 +884,10 @@
</li>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d_nwc">conv1d_nwc() (in module tvm.topi.nn)</a>
</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/relay/nn.html#tvm.relay.nn.conv1d_transpose">conv1d_transpose() (in module tvm.relay.nn)</a>
</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d_transpose_ncw">conv1d_transpose_ncw() (in module tvm.topi.nn)</a>
</li>
<li><a href="reference/api/python/relay/nn.html#tvm.relay.nn.conv2d">conv2d() (in module tvm.relay.nn)</a>
@@ -1038,6 +1038,8 @@
</li>
<li><a href="reference/api/python/te.html#tvm.te.create_prim_func">create_prim_func() (in module tvm.te)</a>
</li>
+ <li><a href="reference/api/python/te.html#tvm.te.create_prim_func_from_outputs">create_prim_func_from_outputs() (in module tvm.te)</a>
+</li>
<li><a href="reference/api/python/runtime.html#tvm.runtime.Device.create_raw_stream">create_raw_stream() (tvm.runtime.Device method)</a>
</li>
<li><a href="reference/api/python/te.html#tvm.te.create_schedule">create_schedule() (in module tvm.te)</a>
@@ -1290,6 +1292,8 @@
</li>
<li><a href="reference/api/python/autotvm.html#tvm.autotvm.record.encode">encode() (in module tvm.autotvm.record)</a>
</li>
+ <li><a href="reference/api/python/ir.html#tvm.instrument.PassInstrument.enter_pass_ctx">enter_pass_ctx() (tvm.instrument.PassInstrument method)</a>
+</li>
<li><a href="reference/api/python/tir.html#tvm.tir.Schedule.enter_postproc">enter_postproc() (tvm.tir.Schedule method)</a>
</li>
<li><a href="reference/api/python/runtime.html#tvm.runtime.Module.entry_func">entry_func (tvm.runtime.Module property)</a>
@@ -1324,6 +1328,8 @@
</li>
<li><a href="reference/api/python/runtime.html#tvm.runtime.Device.exist">exist (tvm.runtime.Device property)</a>
</li>
+ <li><a href="reference/api/python/ir.html#tvm.instrument.PassInstrument.exit_pass_ctx">exit_pass_ctx() (tvm.instrument.PassInstrument method)</a>
+</li>
<li><a href="reference/api/python/te.html#tvm.te.exp">exp() (in module tvm.te)</a>
<ul>
@@ -1586,6 +1592,8 @@
</li>
<li><a href="reference/api/python/relay/analysis.html#tvm.relay.analysis.get_calibration_data">get_calibration_data() (in module tvm.relay.analysis)</a>
</li>
+ <li><a href="reference/api/python/tir.html#tvm.tir.Schedule.get_child_blocks">get_child_blocks() (tvm.tir.Schedule method)</a>
+</li>
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.vgg.get_classifier">get_classifier() (in module tvm.relay.testing.vgg)</a>
</li>
<li><a href="reference/api/python/topi.html#tvm.topi.image.get_closest_index">get_closest_index() (in module tvm.topi.image)</a>
@@ -1654,6 +1662,8 @@
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.Schedule.get_loops">get_loops() (tvm.tir.Schedule method)</a>
</li>
+ <li><a href="reference/api/python/micro.html#tvm.micro.get_microtvm_template_projects">get_microtvm_template_projects() (in module tvm.micro)</a>
+</li>
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.Prelude.get_name">get_name() (tvm.relay.testing.Prelude method)</a>
</li>
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.Prelude.get_name_static">get_name_static() (tvm.relay.testing.Prelude method)</a>
@@ -1676,10 +1686,10 @@
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.vgg.get_net">(in module tvm.relay.testing.vgg)</a>
</li>
</ul></li>
- <li><a href="reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.get_num_inputs">get_num_inputs() (tvm.contrib.graph_executor.GraphModule method)</a>
-</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.get_num_inputs">get_num_inputs() (tvm.contrib.graph_executor.GraphModule method)</a>
+</li>
<li><a href="reference/api/python/autotvm.html#tvm.autotvm.task.space.AnnotateSpace.get_num_output">get_num_output() (tvm.autotvm.task.space.AnnotateSpace static method)</a>
<ul>
@@ -2272,6 +2282,8 @@
<h2 id="M">M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="reference/api/python/target.html#tvm.target.make_compilation_config">make_compilation_config() (in module tvm.target)</a>
+</li>
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.make_nat_expr">make_nat_expr() (in module tvm.relay.testing)</a>
</li>
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.make_nat_value">make_nat_value() (in module tvm.relay.testing)</a>
@@ -2985,10 +2997,10 @@
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.ScheduleState.replace">replace() (tvm.tir.ScheduleState method)</a>
</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/runtime.html#tvm.runtime.Report">Report (class in tvm.runtime)</a>
</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/rpc.html#tvm.rpc.TrackerSession.request">request() (tvm.rpc.TrackerSession method)</a>
</li>
<li><a href="reference/api/python/rpc.html#tvm.rpc.TrackerSession.request_and_run">request_and_run() (tvm.rpc.TrackerSession method)</a>
@@ -3097,8 +3109,12 @@
</ul></li>
<li><a href="reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.run">run() (tvm.contrib.graph_executor.GraphModule method)</a>
</li>
+ <li><a href="reference/api/python/ir.html#tvm.instrument.PassInstrument.run_after_pass">run_after_pass() (tvm.instrument.PassInstrument method)</a>
+</li>
<li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.run_as_python">run_as_python() (in module tvm.relay.testing)</a>
</li>
+ <li><a href="reference/api/python/ir.html#tvm.instrument.PassInstrument.run_before_pass">run_before_pass() (tvm.instrument.PassInstrument method)</a>
+</li>
</ul></td>
</tr></table>
@@ -3115,6 +3131,8 @@
</li>
<li><a href="reference/api/python/auto_scheduler.html#tvm.auto_scheduler.SketchPolicy.sample_initial_population">sample_initial_population() (tvm.auto_scheduler.SketchPolicy method)</a>
</li>
+ <li><a href="reference/api/python/tir.html#tvm.tir.Schedule.sample_perfect_tile">sample_perfect_tile() (tvm.tir.Schedule method)</a>
+</li>
<li><a href="reference/api/python/auto_scheduler.html#tvm.auto_scheduler.XGBModel.save">save() (tvm.auto_scheduler.XGBModel method)</a>
<ul>
@@ -3219,6 +3237,8 @@
</li>
<li><a href="reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.share_params">share_params() (tvm.contrib.graph_executor.GraphModule method)</a>
</li>
+ <li><a href="reference/api/python/ir.html#tvm.instrument.PassInstrument.should_run">should_run() (tvm.instrument.PassInstrument method)</a>
+</li>
<li><a href="reference/api/python/tir.html#tvm.tir.Schedule.show">show() (tvm.tir.Schedule method)</a>
</li>
<li><a href="reference/api/python/tir.html#tvm.tir.Shuffle">Shuffle (class in tvm.tir)</a>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 3dcffa0..06f0d11 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -549,7 +549,7 @@ class:['truck 0.9266'] left:471 right:83 top:689 bottom:169
class:['bicycle 0.9984'] left:111 right:113 top:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 9.553 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 11.483 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 38d5e14..3b1c310 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -611,7 +611,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 19.648 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 21.217 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 4301698..f1ac850 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -300,18 +300,18 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>06:12.390</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:21.665</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:19.648</strong>: <a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></li>
-<li><p><strong>01:09.553</strong>: <a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></li>
-<li><p><strong>00:54.464</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
-<li><p><strong>00:51.984</strong>: <a class="reference internal" href="from_caffe2.html#sphx-glr-how-to-compile-models-from-caffe2-py"><span class="std std-ref">Compile Caffe2 Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_caffe2.py</span></code>)</p></li>
-<li><p><strong>00:29.934</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
-<li><p><strong>00:26.303</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
-<li><p><strong>00:24.457</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
-<li><p><strong>00:23.142</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
-<li><p><strong>00:11.155</strong>: <a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></li>
-<li><p><strong>00:01.750</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
+<li><p><strong>01:21.217</strong>: <a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></li>
+<li><p><strong>01:11.483</strong>: <a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></li>
+<li><p><strong>00:55.783</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
+<li><p><strong>00:53.696</strong>: <a class="reference internal" href="from_caffe2.html#sphx-glr-how-to-compile-models-from-caffe2-py"><span class="std std-ref">Compile Caffe2 Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_caffe2.py</span></code>)</p></li>
+<li><p><strong>00:30.687</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
+<li><p><strong>00:26.879</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
+<li><p><strong>00:25.078</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
+<li><p><strong>00:23.661</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
+<li><p><strong>00:11.349</strong>: <a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></li>
+<li><p><strong>00:01.831</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/deploy/arm_compute_lib.html b/docs/how_to/deploy/arm_compute_lib.html
index e74da4a..111207c 100644
--- a/docs/how_to/deploy/arm_compute_lib.html
+++ b/docs/how_to/deploy/arm_compute_lib.html
@@ -350,30 +350,29 @@ a performance boost on such devices.</p>
<p>Before installing Arm Compute Library, it is important to know what architecture to build for. One way
to determine this is to use <cite>lscpu</cite> and look for the “Model name” of the CPU. You can then use this to
determine the architecture by looking online.</p>
-<p>We recommend two different ways to build and install ACL:</p>
+<p>TVM only supports a single version of ACL, currently this is v21.08, there are two recommended ways to build and install
+the required libraries:</p>
<ul>
-<li><p>Use the script located at <cite>docker/install/ubuntu_install_arm_compute_lib.sh</cite>. You can use this
-script for building ACL from source natively or for cross-compiling the library on an x86 machine.
-You may need to change the architecture of the device you wish to compile for by altering the
-<cite>target_arch</cite> variable. Binaries will be built from source and installed to the location denoted by
-<cite>install_path</cite>.</p></li>
-<li><p>Alternatively, you can download and use pre-built binaries from:
+<li><p>Use the script located at <cite>docker/install/ubuntu_download_arm_compute_lib_binaries.sh</cite>. You can use this
+script for downloading ACL binaries for the architecture and extensions specified in <cite>target_lib</cite>, these
+will be installed to the location denoted by <cite>install_path</cite>.</p></li>
+<li><p>Alternatively, you can download the pre-built binaries from:
<a class="reference external" href="https://github.com/ARM-software/ComputeLibrary/releases">https://github.com/ARM-software/ComputeLibrary/releases</a>. When using this package, you will need to
-select the binaries for the architecture you require and make sure they are visible to cmake. This
-can be done like so:</p>
+select the binaries for the architecture and extensions you require, then make sure they are visible
+to CMake:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> <acl-prebuilt-package>/lib
-mv ./linux-<architecture-to-build-for>-neon/* .
+mv ./<architecture-and-extensions-required>/* .
</pre></div>
</div>
</li>
</ul>
<p>In both cases you will need to set USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR to the path where the ACL package
-is located. Cmake will look in /path-to-acl/ along with /path-to-acl/lib and /path-to-acl/build for the
+is located. CMake will look in /path-to-acl/ along with /path-to-acl/lib and /path-to-acl/build for the
required binaries. See the section below for more information on how to use these configuration options.</p>
</div>
<div class="section" id="building-with-acl-support">
<h2>Building with ACL support<a class="headerlink" href="#building-with-acl-support" title="Permalink to this headline">¶</a></h2>
-<p>The current implementation has two separate build options in cmake. The reason for this split is
+<p>The current implementation has two separate build options in CMake. The reason for this split is
because ACL cannot be used on an x86 machine. However, we still want to be able compile an ACL
runtime module on an x86 machine.</p>
<ul class="simple">
@@ -386,7 +385,7 @@ to compile an ACL module on an x86 machine and then run the module on a remote A
need to use USE_ARM_COMPUTE_LIB=ON on the x86 machine and USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR=ON on the remote
AArch64 device.</p>
<p>By default both options are set to OFF. Using USE_ARM_COMPUTE_LIB_GRAPH_EXECUTOR=ON will mean that ACL
-binaries are searched for by cmake in the default locations
+binaries are searched for by CMake in the default locations
(see <a class="reference external" href="https://cmake.org/cmake/help/v3.4/command/find_library.html">https://cmake.org/cmake/help/v3.4/command/find_library.html</a>). In addition to this,
/path-to-tvm-project/acl/ will also be searched. It is likely that you will need to set your own path to
locate ACL. This can be done by specifying a path in the place of ON.</p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index c6830a4..d6da3d2 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -622,7 +622,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 10.2961 10.1803 11.3268 10.0611 0.3590
+ 11.9578 11.6688 13.6466 11.5382 0.6720
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index 09841ba..3a18c7b 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -494,7 +494,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 18.454 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 24.172 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index 620852d..7dfc231 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -531,7 +531,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 43.7388 43.2618 61.3751 42.8861 1.8866
+ 45.2401 44.8524 49.7695 44.4672 0.8217
</pre></div>
</div>
<div class="admonition note">
@@ -570,7 +570,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.296 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.539 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/fb8217c13f4351224c6cf3aacf1a87fc/deploy_prequantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized_tflite.html b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
index 761d7f9..f5e651f 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -540,7 +540,7 @@ TFLite Top-5 labels: [387 102 386 341 880]
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 67.6108 67.1922 75.4519 66.3023 1.4117
+ 69.3439 69.0170 86.3369 68.2516 1.8214
</pre></div>
</div>
<div class="admonition note">
@@ -568,7 +568,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 18.311 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 22.142 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index 4a11043..4914d25 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -451,7 +451,7 @@ to your device.</p>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" />
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 9.856 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 12.207 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_ssd_gluoncv.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 7d8b637..96a0205 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>11:28.760</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:47.973</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>03:09.856</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
-<li><p><strong>02:18.454</strong>: <a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></li>
-<li><p><strong>02:18.311</strong>: <a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></li>
-<li><p><strong>01:36.296</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
-<li><p><strong>00:55.890</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
-<li><p><strong>00:40.856</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
-<li><p><strong>00:28.940</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
-<li><p><strong>00:00.156</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
+<li><p><strong>03:12.207</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
+<li><p><strong>02:24.172</strong>: <a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></li>
+<li><p><strong>02:22.142</strong>: <a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></li>
+<li><p><strong>01:38.539</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
+<li><p><strong>00:58.984</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
+<li><p><strong>00:42.210</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
+<li><p><strong>00:29.565</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
+<li><p><strong>00:00.154</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index d66b4fe..4a5075d 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -300,12 +300,12 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.477</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:35.149</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:31.129</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
-<li><p><strong>00:02.109</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
-<li><p><strong>00:01.078</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
-<li><p><strong>00:00.161</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
+<li><p><strong>00:31.761</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
+<li><p><strong>00:02.150</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
+<li><p><strong>00:01.081</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
+<li><p><strong>00:00.158</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 42f4014..92b1f17 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -486,10 +486,10 @@ profile the execution time of each passes.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 5337us [5337us] (45.74%; 45.74%)
-FoldScaleAxis: 6330us [2us] (54.26%; 54.26%)
- FoldConstant: 6328us [1368us] (54.24%; 99.97%)
- InferType: 4960us [4960us] (42.51%; 78.38%)
+InferType: 5431us [5431us] (46.27%; 46.27%)
+FoldScaleAxis: 6306us [2us] (53.73%; 53.73%)
+ FoldConstant: 6304us [1297us] (53.71%; 99.97%)
+ InferType: 5007us [5007us] (42.66%; 79.42%)
</pre></div>
</div>
</div>
@@ -512,10 +512,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 4949us [4949us] (43.04%; 43.04%)
-FoldScaleAxis: 6549us [2us] (56.96%; 56.96%)
- FoldConstant: 6548us [1335us] (56.95%; 99.97%)
- InferType: 5212us [5212us] (45.33%; 79.61%)
+InferType: 5082us [5082us] (44.55%; 44.55%)
+FoldScaleAxis: 6326us [2us] (55.45%; 55.45%)
+ FoldConstant: 6325us [1337us] (55.44%; 99.97%)
+ InferType: 4988us [4988us] (43.72%; 78.86%)
</pre></div>
</div>
<p>Register empty list to clear existing instruments.</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index 6696a45..0f5be8e 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -534,7 +534,7 @@ latency of convolution.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 41.937872 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.269562 ms
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-optimize-operators-opt-conv-cuda-py">
diff --git a/docs/how_to/optimize_operators/opt_conv_tensorcore.html b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
index 96e2c39..e920166 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -782,8 +782,8 @@ by mapping the 2D convolution to tensor intrinsics</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A_1: handle, W_1: handle, Conv_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], []),
- Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ buffers = {Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], []),
W: Buffer(W_2: Pointer(float16), float16, [3, 3, 16, 32, 16, 16], [])}
buffer_map = {A_1: A, W_1: W, Conv_1: Conv} {
attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 196;
@@ -863,7 +863,7 @@ be able to run on our build server</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 7.033960 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 7.699643 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/optimize_operators/opt_gemm.html b/docs/how_to/optimize_operators/opt_gemm.html
index b9d70fa..33703b2 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -431,8 +431,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.007397
-Baseline: 3.271150
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.009229
+Baseline: 3.340634
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -491,7 +491,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.285778
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.295032
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -556,7 +556,7 @@ vastly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.321452
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.331193
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -615,7 +615,7 @@ the access pattern for A matrix is more cache friendly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.112634
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.113024
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -696,7 +696,7 @@ flattening.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.218060
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.232269
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -780,7 +780,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.200839
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.225938
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -790,8 +790,8 @@ write to C when all the block results are ready.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
allocate(packedB: Pointer(global float32x32), float32x32, [32768]), storage_scope = global;
@@ -865,7 +865,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.059074
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.068730
</pre></div>
</div>
<p>Here is the generated IR after parallelization.</p>
@@ -875,8 +875,8 @@ write to C when all the block results are ready.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
- A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
allocate(packedB: Pointer(global float32x32), float32x32, [32768]), storage_scope = global {
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index e775a4f..f3b5597 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -300,11 +300,11 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:30.629</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:32.374</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:28.400</strong>: <a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></li>
-<li><p><strong>00:01.166</strong>: <a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></li>
-<li><p><strong>00:01.062</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
+<li><p><strong>00:29.986</strong>: <a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></li>
+<li><p><strong>00:01.281</strong>: <a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></li>
+<li><p><strong>00:01.107</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index cfbbec0..c3f8c76 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -300,14 +300,14 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>04:10.173</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>04:13.504</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:10.891</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
-<li><p><strong>00:54.964</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
-<li><p><strong>00:31.129</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
-<li><p><strong>00:18.318</strong>: <a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></li>
-<li><p><strong>00:07.574</strong>: <a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></li>
-<li><p><strong>00:07.298</strong>: <a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></li>
+<li><p><strong>02:13.406</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
+<li><p><strong>00:55.457</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
+<li><p><strong>00:31.830</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
+<li><p><strong>00:17.732</strong>: <a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></li>
+<li><p><strong>00:07.667</strong>: <a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></li>
+<li><p><strong>00:07.412</strong>: <a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index a385629..0bc2b7f 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -979,7 +979,7 @@ primfn(data_1: handle, kernel_1: handle, bias_1: handle, compute_1: handle) ->
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.359 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.362 ms
</pre></div>
</div>
</div>
@@ -1544,7 +1544,7 @@ In the example below we resume the status and do more 5 trials.</p>
Get devices for measurement successfully!
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 10.891 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 13.406 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_conv2d_layer_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index b80cf10..7110e79 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -876,7 +876,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.1213 3.1261 3.1348 3.1030 0.0134
+ 3.1554 3.1548 3.1623 3.1491 0.0054
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 1376a76..d513464 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -895,7 +895,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 542.5096 542.6023 542.7230 542.2034 0.2220
+ 571.8012 571.6694 572.2598 571.4744 0.3339
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
index 709b999..e0e5209 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -594,34 +594,58 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {compute: Buffer(compute_2: Pointer(float32), float32, [128, 512], []),
- placeholder_4: Buffer(placeholder_10: Pointer(float32), float32, [128, 256], []),
+ placeholder_4: Buffer(placeholder_10: Pointer(float32), float32, [128, 512], []),
placeholder_3: Buffer(placeholder_11: Pointer(int32), int32, [33], []),
- placeholder_2: Buffer(placeholder_12: Pointer(float32), float32, [128, 512], []),
+ placeholder_2: Buffer(placeholder_12: Pointer(int32), int32, [4916], []),
placeholder: Buffer(placeholder_13: Pointer(float32), float32, [4916, 16, 1], []),
- placeholder_1: Buffer(placeholder_14: Pointer(int32), int32, [4916], [])}
- buffer_map = {placeholder_6: placeholder, placeholder_7: placeholder_1, placeholder_9: placeholder_2, placeholder_8: placeholder_3, compute_1: compute, placeholder_5: placeholder_4} {
+ placeholder_1: Buffer(placeholder_14: Pointer(float32), float32, [128, 256], [])}
+ buffer_map = {placeholder_6: placeholder, placeholder_5: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute} {
for (i0.outer.i1.outer.fused: int32, 0, 16) "parallel" {
allocate(compute_3: Pointer(global float32), float32, [4096]), storage_scope = global {
- for (i.outer.inner: int32, 0, 2) {
+ for (i.outer.inner: int32, 0, 16) {
for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 64) {
- for (j.init: int32, 0, 16) {
- compute_3[((((i.outer.inner*2048) + (i.inner.init*32)) + (nb_j.inner*16)) + j.init)] = 0f32
- }
+ for (i.inner.init: int32, 0, 8) {
+ compute_3[(((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16))] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 1)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 2)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 3)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 4)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 5)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 6)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 7)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 8)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 9)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 10)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 11)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 12)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 13)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 14)] = 0f32
+ compute_3[((((i.outer.inner*256) + (i.inner.init*32)) + (nb_j.inner*16)) + 15)] = 0f32
}
for (elem_idx: int32, 0, ((int32*)placeholder_11[(((i0.outer.i1.outer.fused*2) + nb_j.inner) + 1)] - (int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)])) {
- for (i.inner: int32, 0, 64) {
- for (j: int32, 0, 16) {
- compute_3[((((i.outer.inner*2048) + (i.inner*32)) + (nb_j.inner*16)) + j)] = ((float32*)compute_3[((((i.outer.inner*2048) + (i.inner*32)) + (nb_j.inner*16)) + j)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_10[(((i.outer.inner*16384) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
- }
+ for (i.inner: int32, 0, 8) {
+ compute_3[(((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16))] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16))] + ((float32*)placeholder_13[(((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16))]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 1)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 1)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 1)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 2)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 2)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 2)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 3)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 3)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 3)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 4)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 4)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 4)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 5)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 5)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 5)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 6)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 6)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 6)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 7)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 7)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 7)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 8)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 8)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 8)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 9)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 9)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 9)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 10)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 10)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 10)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 11)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 11)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 11)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 12)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 12)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 12)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 13)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 13)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 13)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 14)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 14)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 14)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+ compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 15)] = ((float32*)compute_3[((((i.outer.inner*256) + (i.inner*32)) + (nb_j.inner*16)) + 15)] + ((float32*)placeholder_13[((((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)]*16) + (elem_idx*16)) + 15)]*max((float32*)placeholder_14[(((i.outer.inner*2048) + (i.inner*256)) + (int32*)placeholder_12[((int32*)placeholder_11[((i0.outer.i1.outer.fused*2) + nb_j.inner)] + elem_idx)])], 0f32)))
}
}
}
}
for (i0.inner: int32, 0, 128) {
- for (i1.inner: int32, 0, 32) {
- compute_2[(((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)] = max(((float32*)compute_3[((i0.inner*32) + i1.inner)] + (float32*)placeholder_12[(((i0.inner*512) + (i0.outer.i1.outer.fused*32)) + i1.inner)]), 0f32)
- }
+ compute_2[ramp(((i0.inner*512) + (i0.outer.i1.outer.fused*32)), 1, 32)] = max(((float32x32*)compute_3[ramp((i0.inner*32), 1, 32)] + (float32x32*)placeholder_10[ramp(((i0.inner*512) + (i0.outer.i1.outer.fused*32)), 1, 32)]), broadcast(0f32, 32))
}
}
}
@@ -660,7 +684,7 @@ primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, plac
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.577 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.847 ms
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index f44b950..8622af6 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:38.221</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:40.937</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:37.517</strong>: <a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></li>
-<li><p><strong>00:00.184</strong>: <a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></li>
-<li><p><strong>00:00.177</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
-<li><p><strong>00:00.172</strong>: <a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></li>
-<li><p><strong>00:00.171</strong>: <a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></li>
+<li><p><strong>00:40.250</strong>: <a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></li>
+<li><p><strong>00:00.186</strong>: <a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></li>
+<li><p><strong>00:00.169</strong>: <a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></li>
+<li><p><strong>00:00.167</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
+<li><p><strong>00:00.166</strong>: <a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index c325f4f..9c848fc 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -525,26 +525,26 @@ for this template</p>
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
-No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 6 GFLOPS: 87.21/87.21 result: MeasureResult(costs=(0.0026544224565217393,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.523228645324707, timestamp=1635354481.0967414) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
-No: 7 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 8 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 9 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 10 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(TimeoutError(),), error_no=MeasureErrorNo.BUILD_TIMEOUT, all_cost=10, timestamp=1635354492.052086) [('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
-No: 11 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 12 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 13 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 14 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 15 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 16 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 17 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 18 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
-No: 19 GFLOPS: 0.00/87.21 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 60: 0xffffffffffffffff\n 59: _start\n 58: __libc_start_main\n 57: main\n 56: Py_Main\n 55: 0x000000000063886a\n 54: PyObject_Call\n 53: 0x000000000058945c\n 52: 0x0000000000507cd3\n 51: _PyEval_EvalFrameDefault\n 50: 0x000000000050a3fc\n 49: 0x00000000005099ff\n 48: 0x0000000000507cd3\n 47: _PyEval_EvalFrameDefault\n 46: 0x000000000050a22e\n 45: 0x0000000000 [...]
-No: 20 GFLOPS: 143.62/143.62 result: MeasureResult(costs=(0.00161187607,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.2787423133850098, timestamp=1635354501.6196096) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 6 GFLOPS: 60.26/60.26 result: MeasureResult(costs=(0.003841781068965517,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.627082109451294, timestamp=1636191415.8784292) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+No: 7 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 8 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 9 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 10 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(TimeoutError(),), error_no=MeasureErrorNo.BUILD_TIMEOUT, all_cost=10, timestamp=1636191426.8456671) [('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
+No: 11 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 12 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 13 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 14 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 15 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 16 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 17 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 18 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 24: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 23: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 22: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime:: [...]
+No: 19 GFLOPS: 0.00/60.26 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 60: 0xffffffffffffffff\n 59: _start\n 58: __libc_start_main\n 57: main\n 56: Py_Main\n 55: 0x000000000063886a\n 54: PyObject_Call\n 53: 0x000000000058945c\n 52: 0x0000000000507cd3\n 51: _PyEval_EvalFrameDefault\n 50: 0x000000000050a3fc\n 49: 0x00000000005099ff\n 48: 0x0000000000507cd3\n 47: _PyEval_EvalFrameDefault\n 46: 0x000000000050a22e\n 45: 0x0000000000 [...]
+No: 20 GFLOPS: 144.48/144.48 result: MeasureResult(costs=(0.001602333111111111,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.0883064270019531, timestamp=1636191436.3377938) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
</pre></div>
</div>
<p>Finally we can inspect the best config from log file, check correctness,
@@ -583,7 +583,7 @@ and measure running time.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best config:
[('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
-Time cost of this operator: 0.001986
+Time cost of this operator: 0.001954
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index ad6f942..a89fd4a 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -428,12 +428,8 @@ both builder and runner to generates multiple measurements for auto tunner.</p>
from Zephyr RTOS. If you choose pass <cite>–platform=host</cite> to this tutorial it will uses x86. You can
choose other options by choosing from <cite>PLATFORM</cite> list.</p>
</div></blockquote>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">repo_root</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span>
- <span class="n">subprocess</span><span class="o">.</span><span class="n">check_output</span><span class="p">([</span><span class="s2">"git"</span><span class="p">,</span> <span class="s2">"rev-parse"</span><span class="p">,</span> <span class="s2">"--show-toplevel"</span><span class="p">],</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span><span class="o">.</span><span class="n"> [...]
-<span class="p">)</span>
-
-<span class="n">module_loader</span> <span class="o">=</span> <a href="../../reference/api/python/micro.html#tvm.micro.AutoTvmModuleLoader" title="View documentation for tvm.micro.AutoTvmModuleLoader"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">AutoTvmModuleLoader</span></a><span class="p">(</span>
- <span class="n">template_project_dir</span><span class="o">=</span><span class="n">repo_root</span> <span class="o">/</span> <span class="s2">"src"</span> <span class="o">/</span> <span class="s2">"runtime"</span> <span class="o">/</span> <span class="s2">"crt"</span> <span class="o">/</span> <span class="s2">"host"</span><span class="p">,</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">module_loader</span> <span class="o">=</span> <a href="../../reference/api/python/micro.html#tvm.micro.AutoTvmModuleLoader" title="View documentation for tvm.micro.AutoTvmModuleLoader"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">AutoTvmModuleLoader</span></a><span class="p">(</span>
+ <span class="n">template_project_dir</span><span class="o">=</span><span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.get_microtvm_template_projects" title="View documentation for tvm.micro.get_microtvm_template_projects"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">get_microtvm_template_projects</span> [...]
<span class="n">project_options</span><span class="o">=</span><span class="p">{</span><span class="s2">"verbose"</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
<span class="p">)</span>
<span class="n">builder</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">autotvm</span><span class="o">.</span><span class="n">LocalBuilder</span><span class="p">(</span>
@@ -449,7 +445,7 @@ choose other options by choosing from <cite>PLATFORM</cite> list.</p>
<span class="c1"># Compiling for physical hardware</span>
<span class="c1"># --------------------------------------------------------------------------</span>
<span class="c1"># module_loader = tvm.micro.AutoTvmModuleLoader(</span>
-<span class="c1"># template_project_dir=repo_root / "apps" / "microtvm" / "zephyr" / "template_project",</span>
+<span class="c1"># template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")),</span>
<span class="c1"># project_options={</span>
<span class="c1"># "zephyr_board": BOARD,</span>
<span class="c1"># "west_cmd": "west",</span>
@@ -500,7 +496,7 @@ the tuned operator.</p>
<span class="n">temp_dir</span> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.tempdir" title="View documentation for tvm.contrib.utils.tempdir"><span class="n">tvm</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">tempdir</span></a><span class="p">()</span>
<span class="n">project</span> <span class="o">=</span> <a href="../../reference/api/python/micro.html#tvm.micro.generate_project" title="View documentation for tvm.micro.generate_project"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">generate_project</span></a><span class="p">(</span>
- <span class="nb">str</span><span class="p">(</span><span class="n">repo_root</span> <span class="o">/</span> <span class="s2">"src"</span> <span class="o">/</span> <span class="s2">"runtime"</span> <span class="o">/</span> <span class="s2">"crt"</span> <span class="o">/</span> <span class="s2">"host"</span><span class="p">),</span>
+ <span class="nb">str</span><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.get_microtvm_template_projects" title="View documentation for tvm.micro.get_microtvm_template_projects"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">get_microtvm_template_projects</span></a><span class="p">(</span><span class="s2">"crt"</span><span class="p">)),</span>
<span class="n">lowered</span><span class="p">,</span>
<span class="n">temp_dir</span> <span class="o">/</span> <span class="s2">"project"</span><span class="p">,</span>
<span class="p">{</span><span class="s2">"verbose"</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -509,7 +505,7 @@ the tuned operator.</p>
<span class="c1"># Compiling for physical hardware</span>
<span class="c1"># --------------------------------------------------------------------------</span>
<span class="c1"># project = tvm.micro.generate_project(</span>
-<span class="c1"># str(repo_root / "apps" / "microtvm" / "zephyr" / "template_project"),</span>
+<span class="c1"># str(tvm.micro.get_microtvm_template_projects("zephyr")),</span>
<span class="c1"># lowered,</span>
<span class="c1"># temp_dir / "project",</span>
<span class="c1"># {</span>
@@ -536,10 +532,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 299.0 98.716 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 2.971 0.981 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.919 0.303 (1, 1, 10, 10, 3) 1 1
-Total_time - 302.89 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 309.9 98.726 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 3.076 0.98 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.922 0.294 (1, 1, 10, 10, 3) 1 1
+Total_time - 313.898 - - - -
</pre></div>
</div>
</div>
@@ -555,7 +551,7 @@ Total_time -
<span class="n">temp_dir</span> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.tempdir" title="View documentation for tvm.contrib.utils.tempdir"><span class="n">tvm</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">tempdir</span></a><span class="p">()</span>
<span class="n">project</span> <span class="o">=</span> <a href="../../reference/api/python/micro.html#tvm.micro.generate_project" title="View documentation for tvm.micro.generate_project"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">generate_project</span></a><span class="p">(</span>
- <span class="nb">str</span><span class="p">(</span><span class="n">repo_root</span> <span class="o">/</span> <span class="s2">"src"</span> <span class="o">/</span> <span class="s2">"runtime"</span> <span class="o">/</span> <span class="s2">"crt"</span> <span class="o">/</span> <span class="s2">"host"</span><span class="p">),</span>
+ <span class="nb">str</span><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.get_microtvm_template_projects" title="View documentation for tvm.micro.get_microtvm_template_projects"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">get_microtvm_template_projects</span></a><span class="p">(</span><span class="s2">"crt"</span><span class="p">)),</span>
<span class="n">lowered_tuned</span><span class="p">,</span>
<span class="n">temp_dir</span> <span class="o">/</span> <span class="s2">"project"</span><span class="p">,</span>
<span class="p">{</span><span class="s2">"verbose"</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -564,7 +560,7 @@ Total_time -
<span class="c1"># Compiling for physical hardware</span>
<span class="c1"># --------------------------------------------------------------------------</span>
<span class="c1"># project = tvm.micro.generate_project(</span>
-<span class="c1"># str(repo_root / "apps" / "microtvm" / "zephyr" / "template_project"),</span>
+<span class="c1"># str(tvm.micro.get_microtvm_template_projects("zephyr")),</span>
<span class="c1"># lowered_tuned,</span>
<span class="c1"># temp_dir / "project",</span>
<span class="c1"># {</span>
@@ -591,10 +587,10 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 173.6 98.47 (1, 1, 10, 10, 6) 2 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.891 1.072 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.806 0.457 (1, 3, 10, 10, 1) 1 1
-Total_time - 176.297 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 177.6 98.456 (1, 1, 10, 10, 6) 2 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 1.952 1.082 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 0.833 0.462 (1, 3, 10, 10, 1) 1 1
+Total_time - 180.385 - - - -
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
diff --git a/docs/how_to/work_with_microtvm/micro_tflite.html b/docs/how_to/work_with_microtvm/micro_tflite.html
index cf7556e..1c77042 100644
--- a/docs/how_to/work_with_microtvm/micro_tflite.html
+++ b/docs/how_to/work_with_microtvm/micro_tflite.html
@@ -572,10 +572,7 @@ the example below the x86 arch is selected and a x86 VM is picked up accordingly
<span class="kn">import</span> <span class="nn">subprocess</span>
<span class="kn">import</span> <span class="nn">pathlib</span>
-<span class="n">repo_root</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span>
- <span class="n">subprocess</span><span class="o">.</span><span class="n">check_output</span><span class="p">([</span><span class="s2">"git"</span><span class="p">,</span> <span class="s2">"rev-parse"</span><span class="p">,</span> <span class="s2">"--show-toplevel"</span><span class="p">],</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span><span class="o">.</span><span class="n"> [...]
-<span class="p">)</span>
-<span class="n">template_project_path</span> <span class="o">=</span> <span class="n">repo_root</span> <span class="o">/</span> <span class="s2">"src"</span> <span class="o">/</span> <span class="s2">"runtime"</span> <span class="o">/</span> <span class="s2">"crt"</span> <span class="o">/</span> <span class="s2">"host"</span>
+<span class="n">template_project_path</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.get_microtvm_template_projects" title="View documentation for tvm.micro.get_microtvm_template_projects"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">get_microtvm_template_projects</span>< [...]
<span class="n">project_options</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># You can use options to provide platform-specific options through TVM.</span>
<span class="c1"># Compiling for physical hardware (or an emulated board, like the mps_an521)</span>
@@ -583,7 +580,7 @@ the example below the x86 arch is selected and a x86 VM is picked up accordingly
<span class="c1"># For physical hardware, you can try out the Zephyr platform by using a different template project</span>
<span class="c1"># and options:</span>
<span class="c1">#</span>
-<span class="c1"># template_project_path = repo_root / "apps" / "microtvm" / "zephyr" / "template_project"</span>
+<span class="c1"># template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr"))</span>
<span class="c1"># project_options = {"project_type": "host_driven", zephyr_board": "nucleo_f746zg"}}</span>
<span class="c1"># Create a temporary directory</span>
@@ -609,7 +606,7 @@ the example below the x86 arch is selected and a x86 VM is picked up accordingly
#ifdef __cplusplus
extern "C"
#endif
-TVM_DLL int32_t tvmgen_default_fused_nn_dense_add_nn_relu(void* args, void* arg_type_ids, int32_t num_args, void* out_ret_value, void* out_ret_tcode, void* resource_handle) {
+TVM_DLL int32_t tvmgen_default_fused_nn_dense_add_nn_relu_1(void* args, void* arg_type_ids, int32_t num_args, void* out_ret_value, void* out_ret_tcode, void* resource_handle) {
void* arg0 = (((TVMValue*)args)[0].v_handle);
- .
- ./codegen
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 10f2e23..ecb17fd 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -300,11 +300,11 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:44.423</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>00:45.172</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:40.952</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with micro TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
-<li><p><strong>00:03.314</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
-<li><p><strong>00:00.157</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:41.823</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with micro TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
+<li><p><strong>00:03.195</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
+<li><p><strong>00:00.154</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 25eb402..c93888b 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -300,10 +300,10 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.987</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:04.310</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.454</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
-<li><p><strong>00:01.532</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
+<li><p><strong>00:02.640</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
+<li><p><strong>00:01.670</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/scan.html b/docs/how_to/work_with_schedules/scan.html
index 2d83cc6..827f478 100644
--- a/docs/how_to/work_with_schedules/scan.html
+++ b/docs/how_to/work_with_schedules/scan.html
@@ -512,22 +512,22 @@ The following example demonstrates how we can build recurrence with two states.<
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(X_1: handle, scan_2: handle, scan_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {X: Buffer(X_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- scan_1: Buffer(scan_4: Pointer(float32), float32, [m, l: int32], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {scan_1: Buffer(scan_4: Pointer(float32), float32, [m: int32, l: int32], [stride: int32, stride_1: int32], type="auto"),
+ X: Buffer(X_2: Pointer(float32), float32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
scan: Buffer(scan_5: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {X_1: X, scan_2: scan, scan_3: scan_1} {
for (i: int32, 0, n) {
- scan_5[(i*stride_5)] = (float32*)X_2[(i*stride_1)]
+ scan_5[(i*stride_5)] = (float32*)X_2[(i*stride_3)]
}
for (i_1: int32, 0, l) {
- scan_4[(i_1*stride_3)] = 0f32
+ scan_4[(i_1*stride_1)] = 0f32
}
for (scan.idx: int32, 0, (m - 1)) {
for (i_2: int32, 0, n) {
- scan_5[(((scan.idx + 1)*stride_4) + (i_2*stride_5))] = ((float32*)scan_5[((scan.idx*stride_4) + (i_2*stride_5))] + (float32*)X_2[(((scan.idx + 1)*stride) + (i_2*stride_1))])
+ scan_5[(((scan.idx + 1)*stride_4) + (i_2*stride_5))] = ((float32*)scan_5[((scan.idx*stride_4) + (i_2*stride_5))] + (float32*)X_2[(((scan.idx + 1)*stride_2) + (i_2*stride_3))])
}
for (i_3: int32, 0, l) {
- scan_4[(((scan.idx + 1)*stride_2) + (i_3*stride_3))] = ((float32*)scan_4[((scan.idx*stride_2) + (i_3*stride_3))] + (float32*)scan_5[(scan.idx*stride_4)])
+ scan_4[(((scan.idx + 1)*stride) + (i_3*stride_1))] = ((float32*)scan_4[((scan.idx*stride) + (i_3*stride_1))] + (float32*)scan_5[(scan.idx*stride_4)])
}
}
}
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index f46af8a..e438e46 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:04.941</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.020</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:01.757</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
-<li><p><strong>00:01.128</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
-<li><p><strong>00:00.598</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
-<li><p><strong>00:00.595</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
-<li><p><strong>00:00.296</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
-<li><p><strong>00:00.195</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
-<li><p><strong>00:00.188</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
-<li><p><strong>00:00.185</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
+<li><p><strong>00:01.792</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
+<li><p><strong>00:01.068</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
+<li><p><strong>00:00.669</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
+<li><p><strong>00:00.657</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
+<li><p><strong>00:00.288</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
+<li><p><strong>00:00.186</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
+<li><p><strong>00:00.185</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
+<li><p><strong>00:00.175</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 1edbf9c..845316c 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -546,8 +546,8 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
A: Buffer(A_2: Pointer(float32), float32, [1024, 64], []),
B: Buffer(B_2: Pointer(float32), float32, [512, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp0um40v6k/input0.cc'
-source_filename = "/tmp/tmp0um40v6k/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpdl6d2pzt/input0.cc'
+source_filename = "/tmp/tmpdl6d2pzt/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
diff --git a/docs/how_to/work_with_schedules/tuple_inputs.html b/docs/how_to/work_with_schedules/tuple_inputs.html
index ba2fc3c..470ce0e 100644
--- a/docs/how_to/work_with_schedules/tuple_inputs.html
+++ b/docs/how_to/work_with_schedules/tuple_inputs.html
@@ -371,15 +371,15 @@ together in the next schedule procedure.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A0_1: handle, A1_1: handle, B_2: handle, B_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_4: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- B_1: Buffer(B_5: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+ buffers = {B_1: Buffer(B_4: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B: Buffer(B_5: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, B_2: B, B_3: B_1} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B_4[((i*stride) + (j*stride_1))] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
- B_5[((i*stride_4) + (j*stride_5))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
+ B_5[((i*stride_2) + (j*stride_3))] = ((float32*)A0_2[((i*stride_4) + (j*stride_5))] + 2f32)
+ B_4[((i*stride) + (j*stride_1))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
}
}
}
@@ -426,15 +426,15 @@ with <code class="xref py py-func docutils literal notranslate"><span class="pre
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(idx_1: handle, val_1: handle, T_2: handle, T_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {T_1: Buffer(T_4: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
- idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_1: int32, stride_2: int32], type="auto"),
- T: Buffer(T_5: Pointer(int32), int32, [m], [stride_3: int32], type="auto"),
+ T: Buffer(T_5: Pointer(int32), int32, [m], [stride_1: int32], type="auto"),
+ idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
val: Buffer(val_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {idx_1: idx, val_1: val, T_2: T, T_3: T_1} {
for (i: int32, 0, m) {
- T_5[(i*stride_3)] = -1
+ T_5[(i*stride_1)] = -1
T_4[(i*stride)] = -2147483648
for (k: int32, 0, n) {
- T_5[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T_4[(i*stride)]), (int32*)T_5[(i*stride_3)], (int32*)idx_2[((i*stride_1) + (k*stride_2))], dtype=int32)
+ T_5[(i*stride_1)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T_4[(i*stride)]), (int32*)T_5[(i*stride_1)], (int32*)idx_2[((i*stride_2) + (k*stride_3))], dtype=int32)
T_4[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T_4[(i*stride)]), (int32*)T_4[(i*stride)], (int32*)val_2[((i*stride_4) + (k*stride_5))], dtype=int32)
}
}
@@ -468,19 +468,19 @@ in terms of operation.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primfn(A0_1: handle, A1_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
- buffers = {A0: Buffer(A0_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- C: Buffer(C_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, C_1: C} {
allocate(B.v0: Pointer(global float32), float32, [n]), storage_scope = global;
allocate(B.v1: Pointer(global float32), float32, [n]), storage_scope = global;
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B.v0[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))] + 2f32)
- B.v1[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))]*3f32)
+ B.v0[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
+ B.v1[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))]*3f32)
}
for (j_1: int32, 0, n) {
- C_2[((i*stride_2) + (j_1*stride_3))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
+ C_2[((i*stride) + (j_1*stride_1))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
}
}
}
diff --git a/docs/install/from_source.html b/docs/install/from_source.html
index bb0e810..2d3e59e 100644
--- a/docs/install/from_source.html
+++ b/docs/install/from_source.html
@@ -425,6 +425,8 @@ you want to build for (OpenCL, RCOM, METAL, VULKAN, …).</p></li>
For example, set <code class="docutils literal notranslate"><span class="pre">set(USE_LLVM</span> <span class="pre">llvm-config-10)</span></code> if you installed LLVM 10 package</p></li>
</ul>
</li>
+<li><p>If you are a PyTorch user, it is recommended to set <code class="docutils literal notranslate"><span class="pre">(USE_LLVM</span> <span class="pre">"/path/to/llvm-config</span> <span class="pre">--link-static")</span></code> and <code class="docutils literal notranslate"><span class="pre">set(HIDE_PRIVATE_SYMBOLS</span> <span class="pre">ON)</span></code>
+to avoid potential symbol conflicts between different versions LLVM used by TVM and PyTorch.</p></li>
</ul>
</li>
<li><p>We can then build tvm and related libraries.</p>
diff --git a/docs/langref/hybrid_script.html b/docs/langref/hybrid_script.html
new file mode 100644
index 0000000..23ca02a
--- /dev/null
+++ b/docs/langref/hybrid_script.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/hybrid_script.html" />
+ <script>
+ window.location.href = "../reference/langref/hybrid_script.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/langref/index.html b/docs/langref/index.html
new file mode 100644
index 0000000..5e747f8
--- /dev/null
+++ b/docs/langref/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/index.html" />
+ <script>
+ window.location.href = "../reference/langref/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/langref/relay_adt.html b/docs/langref/relay_adt.html
new file mode 100644
index 0000000..80c8143
--- /dev/null
+++ b/docs/langref/relay_adt.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/relay_adt.html" />
+ <script>
+ window.location.href = "../reference/langref/relay_adt.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/langref/relay_expr.html b/docs/langref/relay_expr.html
new file mode 100644
index 0000000..2dad1dc
--- /dev/null
+++ b/docs/langref/relay_expr.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/relay_expr.html" />
+ <script>
+ window.location.href = "../reference/langref/relay_expr.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/langref/relay_op.html b/docs/langref/relay_op.html
new file mode 100644
index 0000000..ea51943
--- /dev/null
+++ b/docs/langref/relay_op.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/relay_op.html" />
+ <script>
+ window.location.href = "../reference/langref/relay_op.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/langref/relay_pattern.html b/docs/langref/relay_pattern.html
new file mode 100644
index 0000000..478ba63
--- /dev/null
+++ b/docs/langref/relay_pattern.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/relay_pattern.html" />
+ <script>
+ window.location.href = "../reference/langref/relay_pattern.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/langref/relay_type.html b/docs/langref/relay_type.html
new file mode 100644
index 0000000..8989d48
--- /dev/null
+++ b/docs/langref/relay_type.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../reference/langref/relay_type.html" />
+ <script>
+ window.location.href = "../reference/langref/relay_type.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/microtvm/index.html b/docs/microtvm/index.html
new file mode 100644
index 0000000..92f031b
--- /dev/null
+++ b/docs/microtvm/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../topic/microtvm/index.html" />
+ <script>
+ window.location.href = "../topic/microtvm/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/objects.inv b/docs/objects.inv
index 49c09b6..cfbcd8c 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/profiling/index.html b/docs/profiling/index.html
new file mode 100644
index 0000000..f9aeab6
--- /dev/null
+++ b/docs/profiling/index.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/profile/index.html" />
+ <script>
+ window.location.href = "../how_to/profile/index.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/profiling/papi.html b/docs/profiling/papi.html
new file mode 100644
index 0000000..a0d7c35
--- /dev/null
+++ b/docs/profiling/papi.html
@@ -0,0 +1,10 @@
+
+<!DOCTYPE html>
+<html>
+ <head>
+ <meta http-equiv="refresh" content="1; url=../how_to/profile/papi.html" />
+ <script>
+ window.location.href = "../how_to/profile/papi.html"
+ </script>
+ </head>
+</html>
diff --git a/docs/reference/api/doxygen/annotated.html b/docs/reference/api/doxygen/annotated.html
index d66c1bb..bba9394 100644
--- a/docs/reference/api/doxygen/annotated.html
+++ b/docs/reference/api/doxygen/annotated.html
@@ -227,48 +227,56 @@ $(function() {
<tr id="row_1_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html" target="_self">PassInstrument</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" title="PassInstrumentNode forms an instrument implementation. It provides API for us [...]
<tr id="row_1_3_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" target="_self">PassInstrumentNode</a></td><td class="desc"><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" title="PassInstrumentNode forms an instrument implementation. It provides API for users to register call [...]
<tr id="row_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_4_" class="arrow" onclick="toggleFolder('1_4_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1meta__schedule.html" target="_self">meta_schedule</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html" target="_self">ArgInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.html" title="The argument information. ">ArgInfoNode</a> </td></tr>
-<tr id="row_1_4_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.html" target="_self">ArgInfoNode</a></td><td class="desc">The argument information </td></tr>
-<tr id="row_1_4_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html" target="_self">Builder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" title="The abstract builder interface. ">BuilderNode</a> </td></tr>
-<tr id="row_1_4_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html" target="_self">BuilderInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" title="The builder's input, containing an IRModule and the target. ">BuilderInputNode [...]
-<tr id="row_1_4_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" target="_self">BuilderInputNode</a></td><td class="desc">The builder's input, containing an <a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> and the target </td></tr>
-<tr id="row_1_4_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" target="_self">BuilderNode</a></td><td class="desc">The abstract builder interface </td></tr>
-<tr id="row_1_4_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html" target="_self">BuilderResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" title="The builder's output, containing the artifact path or error message if any. [...]
-<tr id="row_1_4_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" target="_self">BuilderResultNode</a></td><td class="desc">The builder's output, containing the artifact path or error message if any </td></tr>
-<tr id="row_1_4_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html" target="_self">Database</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">DatabaseNode</a> </td></tr>
-<tr id="row_1_4_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html" target="_self">DatabaseNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_self">MeasureCandidate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" title="The schedule (with input shapes) to be measured. ">MeasureCandidateNo [...]
-<tr id="row_1_4_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" target="_self">MeasureCandidateNode</a></td><td class="desc">The schedule (with input shapes) to be measured </td></tr>
-<tr id="row_1_4_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyBuilderNode.html" target="_self">PyBuilderNode</a></td><td class="desc">An abstract builder with customized build method on the python-side </td></tr>
-<tr id="row_1_4_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html" target="_self">PyDatabaseNode</a></td><td class="desc">The database with customized methods on the python-side </td></tr>
-<tr id="row_1_4_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyRunnerNode.html" target="_self">PyRunnerNode</a></td><td class="desc">An abstract runner with customized build method on the python-side </td></tr>
-<tr id="row_1_4_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html" target="_self">PySearchStrategyNode</a></td><td class="desc">The python side customizable class for measure candidate generation </td></tr>
-<tr id="row_1_4_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PySpaceGeneratorNode.html" target="_self">PySpaceGeneratorNode</a></td><td class="desc">The design space generator with customized methods on the python-side </td></tr>
-<tr id="row_1_4_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html" target="_self">PyTaskSchedulerNode</a></td><td class="desc">The task scheduler with customized methods on the python-side </td></tr>
-<tr id="row_1_4_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" target="_self">Runner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" title="The abstract runner interface. ">RunnerNode</a> </td></tr>
-<tr id="row_1_4_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_self">RunnerFuture</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" title="A class to asynchronously fetch runner's output. ">RunnerFutureNode</a> </td></tr>
-<tr id="row_1_4_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" target="_self">RunnerFutureNode</a></td><td class="desc">A class to asynchronously fetch runner's output </td></tr>
-<tr id="row_1_4_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_self">RunnerInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" title="Runner's input containing path of artifact, type of device and argument info. "> [...]
-<tr id="row_1_4_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" target="_self">RunnerInputNode</a></td><td class="desc"><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" title="Managed reference to RunnerNode. ">Runner</a>'s input containing path of artifact, type of device and argu [...]
-<tr id="row_1_4_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" target="_self">RunnerNode</a></td><td class="desc">The abstract runner interface </td></tr>
-<tr id="row_1_4_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_self">RunnerResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" title="Runner's output containing measurement result of MeasureCandidate or error ms [...]
-<tr id="row_1_4_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" target="_self">RunnerResultNode</a></td><td class="desc"><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" title="Managed reference to RunnerNode. ">Runner</a>'s output containing measurement result of <a class="el" hr [...]
-<tr id="row_1_4_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_self">SearchStrategy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" title="The search strategy for measure candidates generation. ">SearchStrategyNode [...]
-<tr id="row_1_4_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" target="_self">SearchStrategyNode</a></td><td class="desc">The search strategy for measure candidates generation </td></tr>
-<tr id="row_1_4_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_self">SpaceGenerator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" title="The abstract class for design space generation. ">SpaceGeneratorNode</a> </ [...]
-<tr id="row_1_4_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" target="_self">SpaceGeneratorNode</a></td><td class="desc">The abstract class for design space generation </td></tr>
-<tr id="row_1_4_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_self">TaskScheduler</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedulerNode</a> </td></tr>
-<tr id="row_1_4_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" target="_self">TaskSchedulerNode</a></td><td class="desc">The abstract interface of task schedulers </td></tr>
-<tr id="row_1_4_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfo.html" target="_self">TensorInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" title="The tensor argument information. ">TensorInfoNode</a> </td></tr>
-<tr id="row_1_4_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" target="_self">TensorInfoNode</a></td><td class="desc">The tensor argument information </td></tr>
-<tr id="row_1_4_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_self">TuneContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" title="The auto tuning context. ">TuneContextNode</a> </td></tr>
-<tr id="row_1_4_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" target="_self">TuneContextNode</a></td><td class="desc">The auto tuning context </td></tr>
-<tr id="row_1_4_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_self">TuningRecord</a></td><td class="desc">The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </td></tr>
-<tr id="row_1_4_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" target="_self">TuningRecordNode</a></td><td class="desc">The class of tuning records </td></tr>
-<tr id="row_1_4_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" target="_self">Workload</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" title="A workload, i.e. an IRModule and its structural hash. ">WorkloadNode</a> </td></tr>
-<tr id="row_1_4_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadEqual.html" target="_self">WorkloadEqual</a></td><td class="desc">The equality check for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode. ">Workload</a> </td></tr>
-<tr id="row_1_4_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadHash.html" target="_self">WorkloadHash</a></td><td class="desc">The hash method for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode. ">Workload</a> </td></tr>
-<tr id="row_1_4_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" target="_self">WorkloadNode</a></td><td class="desc">A workload, i.e. an <a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> and its structural hash </td></tr>
+<tr id="row_1_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBest.html" target="_self">ApplyHistoryBest</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html" title="An integration context that allows application of historically best re [...]
+<tr id="row_1_4_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html" target="_self">ApplyHistoryBestNode</a></td><td class="desc">An integration context that allows application of historically best records from a database </td></tr>
+<tr id="row_1_4_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html" target="_self">ArgInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.html" title="The argument information. ">ArgInfoNode</a> </td></tr>
+<tr id="row_1_4_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.html" target="_self">ArgInfoNode</a></td><td class="desc">The argument information </td></tr>
+<tr id="row_1_4_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html" target="_self">Builder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" title="The abstract builder interface. ">BuilderNode</a> </td></tr>
+<tr id="row_1_4_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html" target="_self">BuilderInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" title="The builder's input, containing an IRModule and the target. ">BuilderInputNode [...]
+<tr id="row_1_4_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" target="_self">BuilderInputNode</a></td><td class="desc">The builder's input, containing an <a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> and the target </td></tr>
+<tr id="row_1_4_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" target="_self">BuilderNode</a></td><td class="desc">The abstract builder interface </td></tr>
+<tr id="row_1_4_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html" target="_self">BuilderResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" title="The builder's output, containing the artifact path or error message if any. [...]
+<tr id="row_1_4_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" target="_self">BuilderResultNode</a></td><td class="desc">The builder's output, containing the artifact path or error message if any </td></tr>
+<tr id="row_1_4_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html" target="_self">Database</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">DatabaseNode</a> </td></tr>
+<tr id="row_1_4_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html" target="_self">DatabaseNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_self">ExtractedTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html" title="A tuning task extracted from the high-level IR. ">ExtractedTaskNode</a> </td></tr>
+<tr id="row_1_4_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html" target="_self">ExtractedTaskNode</a></td><td class="desc">A tuning task extracted from the high-level IR </td></tr>
+<tr id="row_1_4_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_self">MeasureCandidate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" title="The schedule (with input shapes) to be measured. ">MeasureCandidateNo [...]
+<tr id="row_1_4_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" target="_self">MeasureCandidateNode</a></td><td class="desc">The schedule (with input shapes) to be measured </td></tr>
+<tr id="row_1_4_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MetaScheduleContext.html" target="_self">MetaScheduleContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MetaScheduleContextNode.html" title="A context manager interface for the integration. ">MetaSched [...]
+<tr id="row_1_4_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MetaScheduleContextNode.html" target="_self">MetaScheduleContextNode</a></td><td class="desc">A context manager interface for the integration </td></tr>
+<tr id="row_1_4_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyBuilderNode.html" target="_self">PyBuilderNode</a></td><td class="desc">An abstract builder with customized build method on the python-side </td></tr>
+<tr id="row_1_4_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html" target="_self">PyDatabaseNode</a></td><td class="desc">The database with customized methods on the python-side </td></tr>
+<tr id="row_1_4_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyRunnerNode.html" target="_self">PyRunnerNode</a></td><td class="desc">An abstract runner with customized build method on the python-side </td></tr>
+<tr id="row_1_4_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PySearchStrategyNode.html" target="_self">PySearchStrategyNode</a></td><td class="desc">The python side customizable class for measure candidate generation </td></tr>
+<tr id="row_1_4_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PySpaceGeneratorNode.html" target="_self">PySpaceGeneratorNode</a></td><td class="desc">The design space generator with customized methods on the python-side </td></tr>
+<tr id="row_1_4_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1PyTaskSchedulerNode.html" target="_self">PyTaskSchedulerNode</a></td><td class="desc">The task scheduler with customized methods on the python-side </td></tr>
+<tr id="row_1_4_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" target="_self">Runner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" title="The abstract runner interface. ">RunnerNode</a> </td></tr>
+<tr id="row_1_4_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_self">RunnerFuture</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" title="A class to asynchronously fetch runner's output. ">RunnerFutureNode</a> </td></tr>
+<tr id="row_1_4_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" target="_self">RunnerFutureNode</a></td><td class="desc">A class to asynchronously fetch runner's output </td></tr>
+<tr id="row_1_4_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_self">RunnerInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" title="Runner's input containing path of artifact, type of device and argument info. "> [...]
+<tr id="row_1_4_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" target="_self">RunnerInputNode</a></td><td class="desc"><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" title="Managed reference to RunnerNode. ">Runner</a>'s input containing path of artifact, type of device and argu [...]
+<tr id="row_1_4_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" target="_self">RunnerNode</a></td><td class="desc">The abstract runner interface </td></tr>
+<tr id="row_1_4_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_self">RunnerResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" title="Runner's output containing measurement result of MeasureCandidate or error ms [...]
+<tr id="row_1_4_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" target="_self">RunnerResultNode</a></td><td class="desc"><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" title="Managed reference to RunnerNode. ">Runner</a>'s output containing measurement result of <a class="el" hr [...]
+<tr id="row_1_4_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_self">SearchStrategy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" title="The search strategy for measure candidates generation. ">SearchStrategyNode [...]
+<tr id="row_1_4_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" target="_self">SearchStrategyNode</a></td><td class="desc">The search strategy for measure candidates generation </td></tr>
+<tr id="row_1_4_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_self">SpaceGenerator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" title="The abstract class for design space generation. ">SpaceGeneratorNode</a> </ [...]
+<tr id="row_1_4_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" target="_self">SpaceGeneratorNode</a></td><td class="desc">The abstract class for design space generation </td></tr>
+<tr id="row_1_4_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskExtraction.html" target="_self">TaskExtraction</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskExtractionNode.html" title="An integration context for task extraction. ">TaskExtractionNode</a> </td></tr>
+<tr id="row_1_4_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskExtractionNode.html" target="_self">TaskExtractionNode</a></td><td class="desc">An integration context for task extraction </td></tr>
+<tr id="row_1_4_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_self">TaskScheduler</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedulerNode</a> </td></tr>
+<tr id="row_1_4_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" target="_self">TaskSchedulerNode</a></td><td class="desc">The abstract interface of task schedulers </td></tr>
+<tr id="row_1_4_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfo.html" target="_self">TensorInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" title="The tensor argument information. ">TensorInfoNode</a> </td></tr>
+<tr id="row_1_4_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" target="_self">TensorInfoNode</a></td><td class="desc">The tensor argument information </td></tr>
+<tr id="row_1_4_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_self">TuneContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" title="The auto tuning context. ">TuneContextNode</a> </td></tr>
+<tr id="row_1_4_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" target="_self">TuneContextNode</a></td><td class="desc">The auto tuning context </td></tr>
+<tr id="row_1_4_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_self">TuningRecord</a></td><td class="desc">The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </td></tr>
+<tr id="row_1_4_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" target="_self">TuningRecordNode</a></td><td class="desc">The class of tuning records </td></tr>
+<tr id="row_1_4_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" target="_self">Workload</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" title="A workload, i.e. an IRModule and its structural hash. ">WorkloadNode</a> </td></tr>
+<tr id="row_1_4_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadEqual.html" target="_self">WorkloadEqual</a></td><td class="desc">The equality check for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode. ">Workload</a> </td></tr>
+<tr id="row_1_4_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1meta__schedule_1_1WorkloadHash.html" target="_self">WorkloadHash</a></td><td class="desc">The hash method for <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" title="Managed reference to WorkloadNode. ">Workload</a> </td></tr>
+<tr id="row_1_4_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" target="_self">WorkloadNode</a></td><td class="desc">A workload, i.e. an <a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> and its structural hash </td></tr>
<tr id="row_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_5_" class="arrow" onclick="toggleFolder('1_5_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1parser.html" target="_self">parser</a></td><td class="desc"></td></tr>
<tr id="row_1_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1Source.html" target="_self">Source</a></td><td class="desc"></td></tr>
<tr id="row_1_5_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1SourceMap.html" target="_self">SourceMap</a></td><td class="desc"></td></tr>
@@ -863,110 +871,115 @@ $(function() {
<tr id="row_1_31_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueEqual.html" target="_self">BaseValueEqual</a></td><td class="desc">Equality definition of base value class </td></tr>
<tr id="row_1_32_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueHash.html" target="_self">BaseValueHash</a></td><td class="desc">Hash definition of base value classes </td></tr>
<tr id="row_1_33_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">Bool</a></td><td class="desc">Boolean constant </td></tr>
-<tr id="row_1_34_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompileError.html" target="_self">CompileError</a></td><td class="desc">Custom Error class to be thrown during compilation </td></tr>
-<tr id="row_1_35_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
-<tr id="row_1_36_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ConstructorNode.html" target="_self">ConstructorNode</a></td><td class="desc">ADT constructor. Constructors compare by pointer equality </td></tr>
-<tr id="row_1_37_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Diagnostic.html" target="_self">Diagnostic</a></td><td class="desc"></td></tr>
-<tr id="row_1_38_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticBuilder.html" target="_self">DiagnosticBuilder</a></td><td class="desc">A wrapper around std::stringstream to build a diagnostic </td></tr>
-<tr id="row_1_39_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContext.html" target="_self">DiagnosticContext</a></td><td class="desc"></td></tr>
-<tr id="row_1_40_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContextNode.html" target="_self">DiagnosticContextNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_41_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticNode.html" target="_self">DiagnosticNode</a></td><td class="desc">A compiler diagnostic message </td></tr>
-<tr id="row_1_42_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRenderer.html" target="_self">DiagnosticRenderer</a></td><td class="desc"></td></tr>
-<tr id="row_1_43_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRendererNode.html" target="_self">DiagnosticRendererNode</a></td><td class="desc">Display diagnostics in a given display format </td></tr>
-<tr id="row_1_44_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode</a> </td></tr>
-<tr id="row_1_45_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrsNode.html" target="_self">DictAttrsNode</a></td><td class="desc">Specialized attribute type that is backed by a map. The <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the [...]
-<tr id="row_1_46_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM's global environment. ">EnvFuncNode</a> </td></tr>
-<tr id="row_1_47_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFuncNode.html" target="_self">EnvFuncNode</a></td><td class="desc">A serializable function backed by TVM's global environment </td></tr>
-<tr id="row_1_48_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1ErrorBuilder.html" target="_self">ErrorBuilder</a></td><td class="desc">A wrapper around std::stringstream to build error </td></tr>
-<tr id="row_1_49_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ErrorReporter.html" target="_self">ErrorReporter</a></td><td class="desc">An abstraction around how errors are stored and reported. Designed to be opaque to users, so we can support a robust and simpler error reporting mode, as well as a more complex mode </td></tr>
-<tr id="row_1_50_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
-<tr id="row_1_51_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImmNode.html" target="_self">FloatImmNode</a></td><td class="desc">Constant floating point literals in the program </td></tr>
-<tr id="row_1_52_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
-<tr id="row_1_53_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncTypeNode.html" target="_self">FuncTypeNode</a></td><td class="desc">Function type </td></tr>
-<tr id="row_1_54_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_1_55_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFuncNode.html" target="_self">GenericFuncNode</a></td><td class="desc">Represents a generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_1_56_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
-<tr id="row_1_57_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVarNode.html" target="_self">GlobalTypeVarNode</a></td><td class="desc">A global type variable that is used for defining new types or type aliases </td></tr>
-<tr id="row_1_58_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
-<tr id="row_1_59_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVarNode.html" target="_self">GlobalVarNode</a></td><td class="desc">Global variable that lives in the top-level module </td></tr>
-<tr id="row_1_60_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNode</a> </td></tr>
-<tr id="row_1_61_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteTypeNode.html" target="_self">IncompleteTypeNode</a></td><td class="desc">Intermediate values that is used to indicate incomplete type during type inference </td></tr>
-<tr id="row_1_62_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
-<tr id="row_1_63_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literals in the program. ">IntImmNode</a> </td></tr>
-<tr id="row_1_64_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImmNode.html" target="_self">IntImmNode</a></td><td class="desc">Constant integer literals in the program </td></tr>
-<tr id="row_1_65_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
-<tr id="row_1_66_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModuleNode.html" target="_self">IRModuleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> that holds functions and type definitions </td></tr>
-<tr id="row_1_67_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
-<tr id="row_1_68_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfoNode.html" target="_self">MemoryInfoNode</a></td><td class="desc">Memory information of special memory region. Use <a class="el" href="classtvm_1_1MemoryInfo.html" title="Defines memory info. ">MemoryInfo</a> as its container type </td></tr>
-<tr id="row_1_69_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor.html" target="_self">NodeFunctor</a></td><td class="desc">A dynamically dispatched functor on the type of the first argument </td></tr>
-<tr id="row_1_70_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html" target="_self">NodeFunctor< R(const ObjectRef &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_71_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
-<tr id="row_1_72_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpAttrMap.html" target="_self">OpAttrMap</a></td><td class="desc">Map<Op,ValueType> used to store meta-information about <a class="el" href="classtvm_1_1Op.html" title="Managed reference class to OpNode. ">Op</a> </td></tr>
-<tr id="row_1_73_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpNode.html" target="_self">OpNode</a></td><td class="desc">Primitive Op(builtin intrinsics) </td></tr>
-<tr id="row_1_74_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpRegEntry.html" target="_self">OpRegEntry</a></td><td class="desc">Helper structure to register operators </td></tr>
-<tr id="row_1_75_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">PointerType</a></td><td class="desc"></td></tr>
-<tr id="row_1_76_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerTypeNode.html" target="_self">PointerTypeNode</a></td><td class="desc">Low-level raw pointer type </td></tr>
-<tr id="row_1_77_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExpr.html" target="_self">PrimExpr</a></td><td class="desc">Reference to <a class="el" href="classtvm_1_1PrimExprNode.html" title="Base node of all primitive expressions. ">PrimExprNode</a> </td></tr>
-<tr id="row_1_78_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExprNode.html" target="_self">PrimExprNode</a></td><td class="desc">Base node of all primitive expressions </td></tr>
-<tr id="row_1_79_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">PrimType</a></td><td class="desc"></td></tr>
-<tr id="row_1_80_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimTypeNode.html" target="_self">PrimTypeNode</a></td><td class="desc">Primitive data types used in the low-level IR </td></tr>
-<tr id="row_1_81_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Range.html" target="_self">Range</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> constainer </td></tr>
-<tr id="row_1_82_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RangeNode.html" target="_self">RangeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> over one dimension </td></tr>
-<tr id="row_1_83_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_83_" class="arrow" onclick="toggleFolder('1_83_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable.html" target="_self">ReflectionVTable</a></td><td class="desc">Virtual function table to support IR/AST node reflection </td></tr>
-<tr id="row_1_83_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" target="_self">Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table. ">Registry</a> of a reflection table </td></tr>
-<tr id="row_1_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExpr.html" target="_self">RelayExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayExprNode.html" title="Base node of all non-primitive expressions. ">RelayExprNode</a> </td></tr>
-<tr id="row_1_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExprNode.html" target="_self">RelayExprNode</a></td><td class="desc">Base node of all non-primitive expressions </td></tr>
-<tr id="row_1_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR. ">RelayRefTypeNode</a> </td></tr>
-<tr id="row_1_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefTypeNode.html" target="_self">RelayRefTypeNode</a></td><td class="desc">Reference <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> High-level Relay IR </td></tr>
-<tr id="row_1_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprPrinter.html" target="_self">ReprPrinter</a></td><td class="desc">A printer class to print the AST/IR nodes </td></tr>
-<tr id="row_1_89_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_89_" class="arrow" onclick="toggleFolder('1_89_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualReducer.html" target="_self">SEqualReducer</a></td><td class="desc">A Reducer class to reduce the structural equality result of two objects </td></tr>
-<tr id="row_1_89_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors. </td></tr>
-<tr id="row_1_90_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_90_" class="arrow" onclick="toggleFolder('1_90_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashReducer.html" target="_self">SHashReducer</a></td><td class="desc">A Reducer class to reduce the structural hash value </td></tr>
-<tr id="row_1_90_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors </td></tr>
-<tr id="row_1_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">SourceName</a></td><td class="desc">The source name of a file span </td></tr>
-<tr id="row_1_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceNameNode.html" target="_self">SourceNameNode</a></td><td class="desc">The name of a source fragment </td></tr>
-<tr id="row_1_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">Span</a></td><td class="desc"></td></tr>
-<tr id="row_1_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SpanNode.html" target="_self">SpanNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
-<tr id="row_1_95_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralEqual.html" target="_self">StructuralEqual</a></td><td class="desc">Content-aware structural equality comparator for objects </td></tr>
-<tr id="row_1_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralHash.html" target="_self">StructuralHash</a></td><td class="desc">Content-aware structural hasing </td></tr>
-<tr id="row_1_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
-<tr id="row_1_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target. ">TargetKindNode</a> </td></tr>
-<tr id="row_1_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindAttrMap.html" target="_self">TargetKindAttrMap</a></td><td class="desc">Map<TargetKind, ValueType> used to store meta-information about <a class="el" href="classtvm_1_1TargetKind.html" title="Managed reference class to TargetKindNode. ">TargetKind</a> </td></tr>
-<tr id="row_1_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindNode.html" target="_self">TargetKindNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a> kind, specifies the kind of the target </td></tr>
-<tr id="row_1_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindRegEntry.html" target="_self">TargetKindRegEntry</a></td><td class="desc">Helper structure to register <a class="el" href="classtvm_1_1TargetKind.html" title="Managed reference class to TargetKindNode. ">TargetKind</a> </td></tr>
-<tr id="row_1_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetNode.html" target="_self">TargetNode</a></td><td class="desc">Compilation target </td></tr>
-<tr id="row_1_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag. ">TargetTagNode</a> </td></tr>
-<tr id="row_1_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagNode.html" target="_self">TargetTagNode</a></td><td class="desc">A target tag </td></tr>
-<tr id="row_1_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagRegEntry.html" target="_self">TargetTagRegEntry</a></td><td class="desc"></td></tr>
-<tr id="row_1_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineType.html" target="_self">TensorAffineType</a></td><td class="desc">Managed reference to AffineTypes </td></tr>
-<tr id="row_1_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineTypeNode.html" target="_self">TensorAffineTypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TensorAffineType.html" title="Managed reference to AffineTypes. ">TensorAffineType</a> representation </td></tr>
-<tr id="row_1_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> </td></tr>
-<tr id="row_1_109_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorTypeNode.html" target="_self">TensorTypeNode</a></td><td class="desc">This is the most commonly used type in relay. <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode. ">TensorType</a> have a fixed dimension, data type </td></tr>
-<tr id="row_1_110_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineType.html" target="_self">TupleAffineType</a></td><td class="desc">Managed reference to TupleAffineTypes </td></tr>
-<tr id="row_1_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineTypeNode.html" target="_self">TupleAffineTypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TupleAffineType.html" title="Managed reference to TupleAffineTypes. ">TupleAffineType</a> representation </td></tr>
-<tr id="row_1_112_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
-<tr id="row_1_113_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleTypeNode.html" target="_self">TupleTypeNode</a></td><td class="desc">The type of tuple values </td></tr>
-<tr id="row_1_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</a> </td></tr>
-<tr id="row_1_115_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
-<tr id="row_1_116_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCallNode.html" target="_self">TypeCallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> function application </td></tr>
-<tr id="row_1_117_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential Constraints in a function. ">TypeConstraintNode</a> </td></tr>
-<tr id="row_1_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraintNode.html" target="_self">TypeConstraintNode</a></td><td class="desc">Potential Constraints in a function </td></tr>
-<tr id="row_1_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
-<tr id="row_1_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeDataNode.html" target="_self">TypeDataNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeData.html" title="Stores all data for an Algebraic Data Type (ADT). ">TypeData</a> container node </td></tr>
-<tr id="row_1_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">TypedEnvFunc</a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc<R(Args..)></a> </td></tr>
-<tr id="row_1_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedEnvFunc< R(Args...)></a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNode inte [...]
-<tr id="row_1_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">TypeFunctor</a></td><td class="desc"></td></tr>
-<tr id="row_1_124_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">TypeFunctor< R(const Type &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions. ">TypeMutator</a> that mutates expressions </td></tr>
-<tr id="row_1_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeNode.html" target="_self">TypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> is the base type of all types </td></tr>
-<tr id="row_1_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
-<tr id="row_1_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelationNode.html" target="_self">TypeRelationNode</a></td><td class="desc">User defined type relation, it is an input-output relation on types </td></tr>
-<tr id="row_1_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
-<tr id="row_1_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporterNode.html" target="_self">TypeReporterNode</a></td><td class="desc">Reporter that reports back to the type resolution information </td></tr>
-<tr id="row_1_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
-<tr id="row_1_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> parameter in functions </td></tr>
-<tr id="row_1_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
-<tr id="row_1_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
+<tr id="row_1_34_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> </td></tr>
+<tr id="row_1_35_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfigNode.html" target="_self">CompilationConfigNode</a></td><td class="desc">Gathers the <code>Targets</code> and distinguished <code>SEScopes</code> in canonical form needed to compile a Relay module. All centralizes any setup and validation logic needed to tr [...]
+<tr id="row_1_36_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompileError.html" target="_self">CompileError</a></td><td class="desc">Custom Error class to be thrown during compilation </td></tr>
+<tr id="row_1_37_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
+<tr id="row_1_38_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ConstructorNode.html" target="_self">ConstructorNode</a></td><td class="desc">ADT constructor. Constructors compare by pointer equality </td></tr>
+<tr id="row_1_39_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Diagnostic.html" target="_self">Diagnostic</a></td><td class="desc"></td></tr>
+<tr id="row_1_40_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticBuilder.html" target="_self">DiagnosticBuilder</a></td><td class="desc">A wrapper around std::stringstream to build a diagnostic </td></tr>
+<tr id="row_1_41_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContext.html" target="_self">DiagnosticContext</a></td><td class="desc"></td></tr>
+<tr id="row_1_42_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContextNode.html" target="_self">DiagnosticContextNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_43_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticNode.html" target="_self">DiagnosticNode</a></td><td class="desc">A compiler diagnostic message </td></tr>
+<tr id="row_1_44_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRenderer.html" target="_self">DiagnosticRenderer</a></td><td class="desc"></td></tr>
+<tr id="row_1_45_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRendererNode.html" target="_self">DiagnosticRendererNode</a></td><td class="desc">Display diagnostics in a given display format </td></tr>
+<tr id="row_1_46_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode</a> </td></tr>
+<tr id="row_1_47_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrsNode.html" target="_self">DictAttrsNode</a></td><td class="desc">Specialized attribute type that is backed by a map. The <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the [...]
+<tr id="row_1_48_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM's global environment. ">EnvFuncNode</a> </td></tr>
+<tr id="row_1_49_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFuncNode.html" target="_self">EnvFuncNode</a></td><td class="desc">A serializable function backed by TVM's global environment </td></tr>
+<tr id="row_1_50_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1ErrorBuilder.html" target="_self">ErrorBuilder</a></td><td class="desc">A wrapper around std::stringstream to build error </td></tr>
+<tr id="row_1_51_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ErrorReporter.html" target="_self">ErrorReporter</a></td><td class="desc">An abstraction around how errors are stored and reported. Designed to be opaque to users, so we can support a robust and simpler error reporting mode, as well as a more complex mode </td></tr>
+<tr id="row_1_52_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
+<tr id="row_1_53_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImmNode.html" target="_self">FloatImmNode</a></td><td class="desc">Constant floating point literals in the program </td></tr>
+<tr id="row_1_54_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
+<tr id="row_1_55_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncTypeNode.html" target="_self">FuncTypeNode</a></td><td class="desc">Function type </td></tr>
+<tr id="row_1_56_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
+<tr id="row_1_57_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFuncNode.html" target="_self">GenericFuncNode</a></td><td class="desc">Represents a generic function that can be specialized on a per-target basis </td></tr>
+<tr id="row_1_58_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
+<tr id="row_1_59_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVarNode.html" target="_self">GlobalTypeVarNode</a></td><td class="desc">A global type variable that is used for defining new types or type aliases </td></tr>
+<tr id="row_1_60_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
+<tr id="row_1_61_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVarNode.html" target="_self">GlobalVarNode</a></td><td class="desc">Global variable that lives in the top-level module </td></tr>
+<tr id="row_1_62_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNode</a> </td></tr>
+<tr id="row_1_63_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteTypeNode.html" target="_self">IncompleteTypeNode</a></td><td class="desc">Intermediate values that is used to indicate incomplete type during type inference </td></tr>
+<tr id="row_1_64_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
+<tr id="row_1_65_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literals in the program. ">IntImmNode</a> </td></tr>
+<tr id="row_1_66_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImmNode.html" target="_self">IntImmNode</a></td><td class="desc">Constant integer literals in the program </td></tr>
+<tr id="row_1_67_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
+<tr id="row_1_68_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModuleNode.html" target="_self">IRModuleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> that holds functions and type definitions </td></tr>
+<tr id="row_1_69_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
+<tr id="row_1_70_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfoNode.html" target="_self">MemoryInfoNode</a></td><td class="desc">Memory information of special memory region. Use <a class="el" href="classtvm_1_1MemoryInfo.html" title="Defines memory info. ">MemoryInfo</a> as its container type </td></tr>
+<tr id="row_1_71_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor.html" target="_self">NodeFunctor</a></td><td class="desc">A dynamically dispatched functor on the type of the first argument </td></tr>
+<tr id="row_1_72_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html" target="_self">NodeFunctor< R(const ObjectRef &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_73_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
+<tr id="row_1_74_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpAttrMap.html" target="_self">OpAttrMap</a></td><td class="desc">Map<Op,ValueType> used to store meta-information about <a class="el" href="classtvm_1_1Op.html" title="Managed reference class to OpNode. ">Op</a> </td></tr>
+<tr id="row_1_75_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpNode.html" target="_self">OpNode</a></td><td class="desc">Primitive Op(builtin intrinsics) </td></tr>
+<tr id="row_1_76_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpRegEntry.html" target="_self">OpRegEntry</a></td><td class="desc">Helper structure to register operators </td></tr>
+<tr id="row_1_77_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">PointerType</a></td><td class="desc"></td></tr>
+<tr id="row_1_78_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerTypeNode.html" target="_self">PointerTypeNode</a></td><td class="desc">Low-level raw pointer type </td></tr>
+<tr id="row_1_79_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExpr.html" target="_self">PrimExpr</a></td><td class="desc">Reference to <a class="el" href="classtvm_1_1PrimExprNode.html" title="Base node of all primitive expressions. ">PrimExprNode</a> </td></tr>
+<tr id="row_1_80_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExprNode.html" target="_self">PrimExprNode</a></td><td class="desc">Base node of all primitive expressions </td></tr>
+<tr id="row_1_81_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">PrimType</a></td><td class="desc"></td></tr>
+<tr id="row_1_82_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimTypeNode.html" target="_self">PrimTypeNode</a></td><td class="desc">Primitive data types used in the low-level IR </td></tr>
+<tr id="row_1_83_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Range.html" target="_self">Range</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> constainer </td></tr>
+<tr id="row_1_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RangeNode.html" target="_self">RangeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> over one dimension </td></tr>
+<tr id="row_1_85_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_85_" class="arrow" onclick="toggleFolder('1_85_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable.html" target="_self">ReflectionVTable</a></td><td class="desc">Virtual function table to support IR/AST node reflection </td></tr>
+<tr id="row_1_85_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" target="_self">Registry</a></td><td class="desc"><a class="el" href="classtvm_1_1ReflectionVTable_1_1Registry.html" title="Registry of a reflection table. ">Registry</a> of a reflection table </td></tr>
+<tr id="row_1_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExpr.html" target="_self">RelayExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayExprNode.html" title="Base node of all non-primitive expressions. ">RelayExprNode</a> </td></tr>
+<tr id="row_1_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExprNode.html" target="_self">RelayExprNode</a></td><td class="desc">Base node of all non-primitive expressions </td></tr>
+<tr id="row_1_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR. ">RelayRefTypeNode</a> </td></tr>
+<tr id="row_1_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefTypeNode.html" target="_self">RelayRefTypeNode</a></td><td class="desc">Reference <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> High-level Relay IR </td></tr>
+<tr id="row_1_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ReprPrinter.html" target="_self">ReprPrinter</a></td><td class="desc">A printer class to print the AST/IR nodes </td></tr>
+<tr id="row_1_91_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_91_" class="arrow" onclick="toggleFolder('1_91_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualReducer.html" target="_self">SEqualReducer</a></td><td class="desc">A Reducer class to reduce the structural equality result of two objects </td></tr>
+<tr id="row_1_91_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors. </td></tr>
+<tr id="row_1_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEScope.html" target="_self">SEScope</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1SEScopeNode.html" title="Describes at compile time where data is to be stored down to the device and memory scope level...">SEScopeNode</a></code> [...]
+<tr id="row_1_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEScopeCache.html" target="_self">SEScopeCache</a></td><td class="desc">A cache of <code>SEScopes</code>. This can be used: </td></tr>
+<tr id="row_1_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SEScopeNode.html" target="_self">SEScopeNode</a></td><td class="desc">Describes at compile time where data is to be stored down to the device and memory scope level, or where execution is to take place, down to the device level. It is a quadruple of: </td></tr>
+<tr id="row_1_95_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_95_" class="arrow" onclick="toggleFolder('1_95_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashReducer.html" target="_self">SHashReducer</a></td><td class="desc">A Reducer class to reduce the structural hash value </td></tr>
+<tr id="row_1_95_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html" target="_self">Handler</a></td><td class="desc">Internal handler that defines custom behaviors </td></tr>
+<tr id="row_1_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">SourceName</a></td><td class="desc">The source name of a file span </td></tr>
+<tr id="row_1_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceNameNode.html" target="_self">SourceNameNode</a></td><td class="desc">The name of a source fragment </td></tr>
+<tr id="row_1_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">Span</a></td><td class="desc"></td></tr>
+<tr id="row_1_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SpanNode.html" target="_self">SpanNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
+<tr id="row_1_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralEqual.html" target="_self">StructuralEqual</a></td><td class="desc">Content-aware structural equality comparator for objects </td></tr>
+<tr id="row_1_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralHash.html" target="_self">StructuralHash</a></td><td class="desc">Content-aware structural hasing </td></tr>
+<tr id="row_1_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
+<tr id="row_1_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target. ">TargetKindNode</a> </td></tr>
+<tr id="row_1_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindAttrMap.html" target="_self">TargetKindAttrMap</a></td><td class="desc">Map<TargetKind, ValueType> used to store meta-information about <a class="el" href="classtvm_1_1TargetKind.html" title="Managed reference class to TargetKindNode. ">TargetKind</a> </td></tr>
+<tr id="row_1_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindNode.html" target="_self">TargetKindNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a> kind, specifies the kind of the target </td></tr>
+<tr id="row_1_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKindRegEntry.html" target="_self">TargetKindRegEntry</a></td><td class="desc">Helper structure to register <a class="el" href="classtvm_1_1TargetKind.html" title="Managed reference class to TargetKindNode. ">TargetKind</a> </td></tr>
+<tr id="row_1_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetNode.html" target="_self">TargetNode</a></td><td class="desc">Compilation target </td></tr>
+<tr id="row_1_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag. ">TargetTagNode</a> </td></tr>
+<tr id="row_1_109_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagNode.html" target="_self">TargetTagNode</a></td><td class="desc">A target tag </td></tr>
+<tr id="row_1_110_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTagRegEntry.html" target="_self">TargetTagRegEntry</a></td><td class="desc"></td></tr>
+<tr id="row_1_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineType.html" target="_self">TensorAffineType</a></td><td class="desc">Managed reference to AffineTypes </td></tr>
+<tr id="row_1_112_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineTypeNode.html" target="_self">TensorAffineTypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TensorAffineType.html" title="Managed reference to AffineTypes. ">TensorAffineType</a> representation </td></tr>
+<tr id="row_1_113_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> </td></tr>
+<tr id="row_1_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorTypeNode.html" target="_self">TensorTypeNode</a></td><td class="desc">This is the most commonly used type in relay. <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode. ">TensorType</a> have a fixed dimension, data type </td></tr>
+<tr id="row_1_115_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineType.html" target="_self">TupleAffineType</a></td><td class="desc">Managed reference to TupleAffineTypes </td></tr>
+<tr id="row_1_116_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineTypeNode.html" target="_self">TupleAffineTypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TupleAffineType.html" title="Managed reference to TupleAffineTypes. ">TupleAffineType</a> representation </td></tr>
+<tr id="row_1_117_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
+<tr id="row_1_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleTypeNode.html" target="_self">TupleTypeNode</a></td><td class="desc">The type of tuple values </td></tr>
+<tr id="row_1_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</a> </td></tr>
+<tr id="row_1_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
+<tr id="row_1_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCallNode.html" target="_self">TypeCallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> function application </td></tr>
+<tr id="row_1_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential Constraints in a function. ">TypeConstraintNode</a> </td></tr>
+<tr id="row_1_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraintNode.html" target="_self">TypeConstraintNode</a></td><td class="desc">Potential Constraints in a function </td></tr>
+<tr id="row_1_124_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
+<tr id="row_1_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeDataNode.html" target="_self">TypeDataNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeData.html" title="Stores all data for an Algebraic Data Type (ADT). ">TypeData</a> container node </td></tr>
+<tr id="row_1_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">TypedEnvFunc</a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc<R(Args..)></a> </td></tr>
+<tr id="row_1_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedEnvFunc< R(Args...)></a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNode inte [...]
+<tr id="row_1_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">TypeFunctor</a></td><td class="desc"></td></tr>
+<tr id="row_1_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">TypeFunctor< R(const Type &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions. ">TypeMutator</a> that mutates expressions </td></tr>
+<tr id="row_1_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeNode.html" target="_self">TypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> is the base type of all types </td></tr>
+<tr id="row_1_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
+<tr id="row_1_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelationNode.html" target="_self">TypeRelationNode</a></td><td class="desc">User defined type relation, it is an input-output relation on types </td></tr>
+<tr id="row_1_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
+<tr id="row_1_135_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporterNode.html" target="_self">TypeReporterNode</a></td><td class="desc">Reporter that reports back to the type resolution information </td></tr>
+<tr id="row_1_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
+<tr id="row_1_137_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> parameter in functions </td></tr>
+<tr id="row_1_138_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
+<tr id="row_1_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
<tr id="row_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structMemoryManagerInterface.html" target="_self">MemoryManagerInterface</a></td><td class="desc"></td></tr>
<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm__workspace__t.html" target="_self">tvm_workspace_t</a></td><td class="desc"></td></tr>
<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
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@@ -81,7 +81,7 @@ Include dependency graph for arg_info.h:</div>
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+<!-- Node145->Node84 -->
+<g id="edge79" class="edge">
+<title>Node145->Node84</title>
+<path fill="none" stroke="#191970" d="M1676.9061,-509.5951C1677.273,-472.6213 1676.2599,-391.0092 1662.5,-324 1648.2227,-254.4713 1614.2359,-175.4677 1601.9927,-148.2858"/>
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+<!-- Node145->Node86 -->
+<g id="edge77" class="edge">
+<title>Node145->Node86</title>
+<path fill="none" stroke="#191970" d="M1681.4735,-510.0871C1699.4279,-440.1509 1760.2744,-204.452 1768.5,-190 1789.0225,-153.9429 1799.9341,-147.3848 1833.5,-123 1858.4685,-104.861 1890.8035,-90.1063 1912.9549,-81.1118"/>
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-<!-- Node142->Node122 -->
+<!-- Node145->Node124 -->
<g id="edge78" class="edge">
-<title>Node142->Node122</title>
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-<polygon fill="#191970" stroke="#191970" points="1641.1039,-522.4648 1634.7298,-530.9278 1644.9903,-528.2869 1641.1039,-522.4648"/>
+<title>Node145->Node124</title>
+<path fill="none" stroke="#191970" d="M1731.2047,-517.9001C1737.7035,-516.5639 1744.247,-515.2379 1750.5,-514 1836.096,-497.0546 1864.1504,-514.2969 1943.5,-478 1999.8665,-452.2163 2003.2739,-428.6665 2052.5,-391 2142.6865,-321.9916 2253.7911,-242.343 2291.9164,-215.1617"/>
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-<title>Node142->Node123</title>
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+<!-- Node145->Node125 -->
+<g id="edge80" class="edge">
+<title>Node145->Node125</title>
+<path fill="none" stroke="#191970" d="M1731.824,-517.8359C1801.812,-503.0437 1913.559,-479.2878 1915.5,-478 1954.8513,-451.8926 1947.2027,-428.0377 1976.5,-391 2032.1184,-320.6871 2106.2827,-242.1737 2132.139,-215.2031"/>
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-<title>Node142->Node130</title>
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-<polygon fill="#191970" stroke="#191970" points="1548.7923,-536.8029 1559.0985,-534.3468 1549.5101,-529.8398 1548.7923,-536.8029"/>
+<!-- Node145->Node126 -->
+<g id="edge81" class="edge">
+<title>Node145->Node126</title>
+<path fill="none" stroke="#191970" d="M1728.0084,-517.4542C1768.179,-507.2451 1819.8556,-492.0634 1836.5,-478 1865.9572,-453.1106 1876.5,-439.5644 1876.5,-401 1876.5,-401 1876.5,-401 1876.5,-339.5 1876.5,-282.5759 1935.6508,-234.9739 1964.302,-215.2555"/>
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-<title>Node154->Node122</title>
-<path fill="none" stroke="#191970" d="M2000.46,-515.3568C1992.4335,-462.7624 1967.9724,-339.207 1906.7941,-257 1893.0312,-238.5064 1870.6774,-224.0039 1854.5253,-215.1647"/>
-<polygon fill="#191970" stroke="#191970" points="1997.0269,-516.0707 2001.9306,-525.4624 2003.9539,-515.0626 1997.0269,-516.0707"/>
+<!-- Node145->Node133 -->
+<g id="edge74" class="edge">
+<title>Node145->Node133</title>
+<path fill="none" stroke="#191970" d="M1606.572,-519.8199C1514.1449,-507.0252 1352.3433,-484.6271 1260.7599,-471.9492"/>
+<polygon fill="#191970" stroke="#191970" points="1606.2812,-523.3129 1616.6667,-521.2173 1607.2411,-516.379 1606.2812,-523.3129"/>
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-<g id="edge97" class="edge">
-<title>Node156->Node131</title>
-<path fill="none" stroke="#191970" d="M393.2788,-462.0211C348.9622,-453.0541 296.564,-439.2348 281.7941,-422 246.5519,-380.8763 255.8803,-308.5107 260.7424,-282.3148"/>
-<polygon fill="#191970" stroke="#191970" points="392.6873,-465.4718 403.1754,-463.9717 394.041,-458.6039 392.6873,-465.4718"/>
+<!-- Node157->Node125 -->
+<g id="edge86" class="edge">
+<title>Node157->Node125</title>
+<path fill="none" stroke="#191970" d="M2261.4567,-504.7931C2249.0136,-455.932 2218.0856,-344.1436 2175.5,-257 2167.9736,-241.5987 2156.574,-225.3318 2149.0826,-215.2975"/>
+<polygon fill="#191970" stroke="#191970" points="2258.0635,-505.6517 2263.8945,-514.4977 2264.8526,-503.9463 2258.0635,-505.6517"/>
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-<g id="edge90" class="edge">
-<title>Node156->Node157</title>
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-<polygon fill="#191970" stroke="#191970" points="451.3134,-455.4107 457.7016,-463.8631 457.9787,-453.2718 451.3134,-455.4107"/>
+<!-- Node159->Node134 -->
+<g id="edge99" class="edge">
+<title>Node159->Node134</title>
+<path fill="none" stroke="#191970" d="M391.7153,-444.9786C375.5673,-424.7421 350.418,-389.7559 338.5,-355 329.9115,-329.9538 329.5362,-298.2919 330.0044,-282.4633"/>
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-<!-- Node157->Node43 -->
+<!-- Node159->Node160 -->
<g id="edge92" class="edge">
-<title>Node157->Node43</title>
-<path fill="none" stroke="#191970" d="M412.1496,-320.28C404.5125,-290.275 394.3554,-231.4512 417.7941,-190 448.5388,-135.6279 515.3166,-103.4481 562.5813,-86.6471"/>
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-<title>Node157->Node62</title>
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-<polygon fill="#191970" stroke="#191970" points="425.5392,-319.4711 423.4755,-329.8631 431.5693,-323.0262 425.5392,-319.4711"/>
+<title>Node159->Node160</title>
+<path fill="none" stroke="#191970" d="M406.8215,-442.7305C407.2569,-415.9497 408.0173,-369.186 408.3438,-349.1068"/>
+<polygon fill="#191970" stroke="#191970" points="403.3201,-442.7928 406.6569,-452.8484 410.3192,-442.9067 403.3201,-442.7928"/>
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-<g id="edge96" class="edge">
-<title>Node157->Node78</title>
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-<polygon fill="#191970" stroke="#191970" points="399.7924,-324.7868 409.092,-329.8631 404.9878,-320.0954 399.7924,-324.7868"/>
+<!-- Node160->Node46 -->
+<g id="edge94" class="edge">
+<title>Node160->Node46</title>
+<path fill="none" stroke="#191970" d="M349.5294,-327.6489C315.7541,-319.4137 273.3023,-306.5457 238.5,-288 147.9639,-239.7546 82.6529,-127.482 61.1557,-86.6729"/>
+<polygon fill="#191970" stroke="#191970" points="348.7248,-331.0551 359.2621,-329.9532 350.3375,-324.2434 348.7248,-331.0551"/>
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-<!-- Node157->Node100 -->
+<!-- Node160->Node65 -->
<g id="edge95" class="edge">
-<title>Node157->Node100</title>
-<path fill="none" stroke="#191970" d="M488.9833,-332.5723C523.0834,-329.4922 564.5209,-326.0957 601.7941,-324 742.961,-316.0627 1738.209,-328.0976 1873.7941,-288 1954.1241,-264.2434 1957.5039,-222.3034 2034.7941,-190 2083.6032,-169.6002 2142.4205,-155.8886 2184.6895,-147.7769"/>
-<polygon fill="#191970" stroke="#191970" points="488.2889,-329.1211 478.6487,-333.5163 488.9257,-336.0921 488.2889,-329.1211"/>
+<title>Node160->Node65</title>
+<path fill="none" stroke="#191970" d="M430.8361,-324.072C471.2816,-296.1354 555.5263,-237.9458 588.6744,-215.0496"/>
+<polygon fill="#191970" stroke="#191970" points="428.6909,-321.2999 422.452,-329.8631 432.6692,-327.0595 428.6909,-321.2999"/>
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-<title>Node157->Node122</title>
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-<title>Node157->Node131</title>
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-<polygon fill="#191970" stroke="#191970" points="385.0184,-329.1454 395.5863,-329.9005 387.7959,-322.72 385.0184,-329.1454"/>
+<!-- Node160->Node81 -->
+<g id="edge98" class="edge">
+<title>Node160->Node81</title>
+<path fill="none" stroke="#191970" d="M349.969,-327.7209C310.5151,-318.4645 263.5454,-304.4177 251.5,-288 235.4855,-266.1724 242.5723,-231.6072 247.3788,-215.0096"/>
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-<title>Node166->Node142</title>
-<path fill="none" stroke="#191970" d="M933.8376,-846.3383C1048.2464,-809.5094 1309.5016,-720.2702 1513.7941,-612 1549.4548,-593.1007 1587.787,-564.6849 1606.7486,-550.0158"/>
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+<!-- Node160->Node103 -->
+<g id="edge97" class="edge">
+<title>Node160->Node103</title>
+<path fill="none" stroke="#191970" d="M479.219,-336.276C647.1381,-328.4029 1066.1954,-307.418 1205.5,-288 1460.5793,-252.4439 1761.6672,-175.1305 1863.1282,-148.0788"/>
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-<title>Node167</title>
-<g id="a_node41"><a xlink:href="node_8h.html" target="_top" xlink:title="Definitions and helper macros for IR/AST nodes. ">
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-</a>
+<!-- Node160->Node125 -->
+<g id="edge93" class="edge">
+<title>Node160->Node125</title>
+<path fill="none" stroke="#191970" d="M479.658,-335.6799C755.0043,-320.7008 1750.7535,-264.6514 2064.5,-221 2075.7101,-219.4403 2087.7452,-217.2627 2098.892,-215.0318"/>
+<polygon fill="#191970" stroke="#191970" points="479.1921,-332.2 469.3967,-336.2373 479.5718,-339.1897 479.1921,-332.2"/>
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+<!-- Node160->Node134 -->
+<g id="edge96" class="edge">
+<title>Node160->Node134</title>
+<path fill="none" stroke="#191970" d="M389.6394,-323.2992C374.6376,-310.4131 354.1569,-292.8207 341.6548,-282.0817"/>
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-<title>Node166->Node167</title>
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+<!-- Node169->Node145 -->
+<g id="edge111" class="edge">
+<title>Node169->Node145</title>
+<path fill="none" stroke="#191970" d="M868.4666,-834.5369C887.3053,-826.2835 907.9328,-815.3974 924.5,-802 996.2464,-743.9807 970.0371,-682.1608 1050.5,-637 1117.5264,-599.3807 1318.8294,-614.4801 1394.5,-601 1486.7392,-584.5683 1593.3702,-554.408 1645.1051,-539.0425"/>
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<!-- Node170 -->
-<g id="node43" class="node">
+<g id="node42" class="node">
<title>Node170</title>
-<g id="a_node43"><a xlink:href="reflection_8h.html" target="_top" xlink:title="Reflection and serialization of compiler IR/AST nodes. ">
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+<!-- Node169->Node170 -->
<g id="edge112" class="edge">
-<title>Node167->Node20</title>
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+<title>Node169->Node170</title>
+<path fill="none" stroke="#191970" d="M775.7269,-832.7151C764.5176,-824.7271 753.6983,-814.4697 747.5,-802 741.3673,-789.6624 739.99,-782.5511 747.5,-771 761.7926,-749.0168 787.8627,-736.343 811.6727,-729.0739"/>
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-<title>Node167->Node27</title>
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+<title>Node173</title>
+<g id="a_node44"><a xlink:href="reflection_8h.html" target="_top" xlink:title="Reflection and serialization of compiler IR/AST nodes. ">
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+<g id="edge120" class="edge">
+<title>Node169->Node173</title>
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+<title>Node170->Node20</title>
+<path fill="none" stroke="#191970" d="M889.4901,-704.9045C924.7647,-685.837 990.3161,-653.0026 1050.5,-637 1199.0618,-597.4981 1241.847,-619.1128 1394.5,-601 1582.3818,-578.7072 1630.1874,-577.9266 1816.5,-545 1826.1832,-543.2887 1836.5561,-541.1575 1846.271,-539.0286"/>
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<div class="ttc" id="classtvm_1_1runtime_1_1TypedPackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1TypedPackedFunc.html">tvm::runtime::TypedPackedFunc</a></div><div class="ttdoc">Please refer to TypedPackedFunc<R(Args..)>. </div><div class="ttdef"><b>Definition:</b> packed_func.h:136</div></div>
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+<div class="ttc" id="classtvm_1_1Target_html"><div class="ttname"><a href="classtvm_1_1Target.html">tvm::Target</a></div><div class="ttdoc">Managed reference class to TargetNode. </div><div class="ttdef"><b>Definition:</b> target.h:132</div></div>
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<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyBuilderNode_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyBuilderNode.html">tvm::meta_schedule::PyBuilderNode</a></div><div class="ttdoc">An abstract builder with customized build method on the python-side. </div><div class="ttdef"><b>Definition:</b> builder.h:130</div></div>
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